Creating lesson asset information

ABSTRACT

A method for execution by a computing entity for creating a multi-disciplined learning tool regarding a topic includes obtaining a first learner approach associated with a first learner of a set of learners. The method further includes creating first lesson asset information regarding the topic for the first learner based on the first learner approach associated with the first learner. The method further includes sending the first lesson asset information to a first learner computing entity associated with the first learner.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 63/026,812,entitled “CONSTRUCTING A LESSON PACKAGE,” filed May 19, 2020, which ishereby incorporated herein by reference in its entirety and made part ofthe present U.S. Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer systems and moreparticularly to computer systems providing educational, training, andentertainment content.

Description of Related Art

Computer systems communicate data, process data, and/or store data. Suchcomputer systems include computing devices that range from wirelesssmart phones, laptops, tablets, personal computers (PC), work stations,personal three-dimensional (3-D) content viewers, and video gamedevices, to data centers where data servers store and provide access todigital content. Some digital content is utilized to facilitateeducation, training, and entertainment. Examples of visual contentincludes electronic books, reference materials, training manuals,classroom coursework, lecture notes, research papers, images, videoclips, sensor data, reports, etc.

A variety of educational systems utilize educational tools andtechniques. For example, an educator delivers educational content tostudents via an education tool of a recorded lecture that has built-infeedback prompts (e.g., questions, verification of viewing, etc.). Theeducator assess a degree of understanding of the educational contentand/or overall competence level of a student from responses to thefeedback prompts.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem in accordance with the present invention;

FIG. 2A is a schematic block diagram of an embodiment of a computingentity of a computing system in accordance with the present invention;

FIG. 2B is a schematic block diagram of an embodiment of a computingdevice of a computing system in accordance with the present invention;

FIG. 3 is a schematic block diagram of another embodiment of a computingdevice of a computing system in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an environmentsensor module of a computing system in accordance with the presentinvention;

FIG. 5A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 5B is a schematic block diagram of an embodiment of arepresentation of a learning experience in accordance with the presentinvention;

FIG. 6 is a schematic block diagram of another embodiment of arepresentation of a learning experience in accordance with the presentinvention;

FIG. 7A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 7B is a schematic block diagram of another embodiment of arepresentation of a learning experience in accordance with the presentinvention;

FIGS. 8A-8C are schematic block diagrams of another embodiment of acomputing system illustrating an example of creating a learningexperience in accordance with the present invention;

FIG. 8D is a logic diagram of an embodiment of a method for creating alearning experience within a computing system in accordance with thepresent invention;

FIGS. 8E, 8F, 8G, 8H, 8J, and 8K are schematic block diagrams of anotherembodiment of a computing system illustrating another example ofcreating a learning experience in accordance with the present invention;

FIGS. 9A, 9B, 9C, 9D, and 9E are schematic block diagrams of anembodiment of a computing system illustrating an example of constructinga lesson package in accordance with the present invention;

FIGS. 10A, 10B, 10C, and 10D are schematic block diagrams of anembodiment of a computing system illustrating an example of constructinga lesson package in accordance with the present invention;

FIGS. 11A, 11B, 11C, and 11D are schematic block diagrams of anembodiment of a computing system illustrating an example of constructinga lesson package in accordance with the present invention;

FIGS. 12A, 12B, 12C, 12D, 12E, 12F, and 12G are schematic block diagramsof an embodiment of a computing system illustrating an example ofcreating lesson asset information in accordance with the presentinvention;

FIGS. 13A, 13B, 13C, and 13D are schematic block diagrams of anembodiment of a computing system illustrating an example of updating alesson package in accordance with the present invention;

FIGS. 14A, 14B, 14C, and 14D are schematic block diagrams of anembodiment of a computing system illustrating an example of executing alesson package in accordance with the present invention;

FIGS. 15A, 15B, and 15C are schematic block diagrams of an embodiment ofa computing system illustrating an example of creating learning outputinformation in accordance with the present invention;

FIGS. 16A, 16B, 16C, and 16D are schematic block diagrams of anembodiment of a computing system illustrating an example of optimizinglearning in accordance with the present invention;

FIGS. 17A, 17B, 17C, and 17D are schematic block diagrams of anembodiment of a computing system illustrating an example of optimizinglearning in accordance with the present invention; and

FIGS. 18A, 18B, 18C, and 18D are schematic block diagrams of anembodiment of a computing system illustrating an example of optimizinglearning in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem 10 that includes a real world environment 12, an environmentsensor module 14, and environment model database 16, a human interfacemodule 18, and a computing entity 20. The real-world environment 12includes places 22, objects 24, instructors 26-1 through 26-N, andlearners 28-1 through 28-N. The computing entity 20 includes anexperience creation module 30, an experience execution module 32, and alearning assets database 34.

The places 22 includes any area. Examples of places 22 includes a room,an outdoor space, a neighborhood, a city, etc. The objects 24 includesthings within the places. Examples of objects 24 includes people,equipment, furniture, personal items, tools, and representations ofinformation (i.e., video recordings, audio recordings, captured text,etc.). The instructors includes any entity (e.g., human or human proxy)imparting knowledge. The learners includes entities trying to gainknowledge and may temporarily serve as an instructor.

In an example of operation of the computing system 10, the experiencecreation module 30 receives environment sensor information 38 from theenvironment sensor module 14 based on environment attributes 36 from thereal world environment 12. The environment sensor information 38includes time-based information (e.g., static snapshot, continuousstreaming) from environment attributes 36 including XYZ positioninformation, place information, and object information (i.e.,background, foreground, instructor, learner, etc.). The XYZ positioninformation includes portrayal in a world space industry standard format(e.g., with reference to an absolute position).

The environment attributes 36 includes detectable measures of thereal-world environment 12 to facilitate generation of amulti-dimensional (e.g., including time) representation of thereal-world environment 12 in a virtual reality and/or augmented realityenvironment. For example, the environment sensor module 14 producesenvironment sensor information 38 associated with a medical examinationroom and a subject human patient (e.g., an MRI). The environment sensormodule 14 is discussed in greater detail with reference to FIG. 4.

Having received the environment sensor information 38, the experiencecreation module 30 accesses the environment model database 16 to recovermodeled environment information 40. The modeled environment information40 includes a synthetic representation of numerous environments (e.g.,model places and objects). For example, the modeled environmentinformation 40 includes a 3-D representation of a typical humancirculatory system. The models include those that are associated withcertain licensing requirements (e.g., copyrights, etc.).

Having received the modeled environment information 40, the experiencecreation module 30 receives instructor information 44 from the humaninterface module 18, where the human interface module 18 receives humaninput/output (I/O) 42 from instructor 26-1. The instructor information44 includes a representation of an essence of communication with aparticipant instructor. The human I/O 42 includes detectable fundamentalforms of communication with humans or human proxies. The human interfacemodule 18 is discussed in greater detail with reference to FIG. 3.

Having received the instructor information 44, the experience creationmodule 30 interprets the instructor information 44 to identify aspectsof a learning experience. A learning experience includes numerousaspects of an encounter between one or more learners and an imparting ofknowledge within a representation of a learning environment thatincludes a place, multiple objects, and one or more instructors. Thelearning experience further includes an instruction portion (e.g., actsto impart knowledge) and an assessment portion (e.g., further actsand/or receiving of learner input) to determine a level of comprehensionof the knowledge by the one or more learners. The learning experiencestill further includes scoring of the level of comprehension andtallying multiple learning experiences to facilitate higher-levelcompetency accreditations (e.g., certificates, degrees, licenses,training credits, experiences completed successfully, etc.).

As an example of the interpreting of the instructor information 44, theexperience creation module 30 identifies a set of concepts that theinstructor desires to impart upon a learner and a set of comprehensionverifying questions and associated correct answers. The experiencecreation module 30 further identifies step-by-step instructorannotations associated with the various objects within the environmentof the learning experience for the instruction portion and theassessment portion. For example, the experience creation module 30identifies positions held by the instructor 26-1 as the instructornarrates a set of concepts associated with the subject patientcirculatory system. As a further example, the experience creation module30 identifies circulatory system questions and correct answers posed bythe instructor associated with the narrative.

Having interpreted the instructor information 44, the experiencecreation module 30 renders the environment sensor information 38, themodeled environment information 40, and the instructor information 44 toproduce learning assets information 48 for storage in the learningassets database 34. The learning assets information 48 includes allthings associated with the learning experience to facilitate subsequentrecreation. Examples includes the environment, places, objects,instructors, learners, assets, recorded instruction information,learning evaluation information, etc.

Execution of a learning experience for the one or more learners includesa variety of approaches. A first approach includes the experienceexecution module 32 recovering the learning assets information 48 fromthe learning assets database 34, rendering the learning experience aslearner information 46, and outputting the learner information 46 viathe human interface module 18 as further human I/O 42 to one or more ofthe learners 28-1 through 28-N. The learner information 46 includesinformation to be sent to the one or more learners and informationreceived from the one or more learners. For example, the experienceexecution module 32 outputs learner information 46 associated with theinstruction portion for the learner 28-1 and collects learnerinformation 46 from the learner 28-1 that includes submitted assessmentanswers in response to assessment questions of the assessment portioncommunicated as further learner information 46 for the learner 28-1.

A second approach includes the experience execution module 32 renderingthe learner information 46 as a combination of live streaming ofenvironment sensor information 38 from the real-world environment 12along with an augmented reality overlay based on recovered learningasset information 48. For example, a real world subject human patient ina medical examination room is live streamed as the environment sensorinformation 38 in combination with a prerecorded instruction portionfrom the instructor 26-1.

FIG. 2A is a schematic block diagram of an embodiment of the computingentity 20 of the computing system 10. The computing entity 20 includesone or more computing devices 100-1 through 100-N. A computing device isany electronic device that communicates data, processes data, representsdata (e.g., user interface) and/or stores data.

Computing devices include portable computing devices and fixed computingdevices. Examples of portable computing devices include an embeddedcontroller, a smart sensor, a social networking device, a gaming device,a smart phone, a laptop computer, a tablet computer, a video gamecontroller, and/or any other portable device that includes a computingcore. Examples of fixed computing devices includes a personal computer,a computer server, a cable set-top box, a fixed display device, anappliance, and industrial controller, a video game counsel, a homeentertainment controller, a critical infrastructure controller, and/orany type of home, office or cloud computing equipment that includes acomputing core.

FIG. 2B is a schematic block diagram of an embodiment of a computingdevice 100 of the computing system 10 that includes one or morecomputing cores 52-1 through 52-N, a memory module 102, the humaninterface module 18, the environment sensor module 14, and an I/O module104. In alternative embodiments, the human interface module 18, theenvironment sensor module 14, the I/O module 104, and the memory module102 and to may be standalone (e.g., external to the computing device).An embodiment of the computing device 100 will be discussed in greaterdetail with reference to FIG. 3.

FIG. 3 is a schematic block diagram of another embodiment of thecomputing device 100 of the computing system 10 that includes the humaninterface module 18, the environment sensor module 14, the computingcore 52-1, the memory module 102, and the I/O module 104. The humaninterface module 18 includes one or more visual output devices 74 (e.g.,video graphics display, 3-D viewer, touchscreen, LED, etc.), one or morevisual input devices 80 (e.g., a still image camera, a video camera, a3-D video camera, photocell, etc.), and one or more audio output devices78 (e.g., speaker(s), headphone jack, a motor, etc.). The humaninterface module 18 further includes one or more user input devices 76(e.g., keypad, keyboard, touchscreen, voice to text, a push button, amicrophone, a card reader, a door position switch, a biometric inputdevice, etc.) and one or more motion output devices 106 (e.g., servos,motors, lifts, pumps, actuators, anything to get real-world objects tomove).

The computing core 52-1 includes a video graphics module 54, one or moreprocessing modules 50-1 through 50-N, a memory controller 56, one ormore main memories 58-1 through 58-N (e.g., RAM), one or moreinput/output (I/O) device interface modules 62, an input/output (I/O)controller 60, and a peripheral interface 64. A processing module is asdefined at the end of the detailed description.

The memory module 102 includes a memory interface module 70 and one ormore memory devices, including flash memory devices 92, hard drive (HD)memory 94, solid state (SS) memory 96, and cloud memory 98. The cloudmemory 98 includes an on-line storage system and an on-line backupsystem.

The I/O module 104 includes a network interface module 72, a peripheraldevice interface module 68, and a universal serial bus (USB) interfacemodule 66. Each of the I/O device interface module 62, the peripheralinterface 64, the memory interface module 70, the network interfacemodule 72, the peripheral device interface module 68, and the USBinterface modules 66 includes a combination of hardware (e.g.,connectors, wiring, etc.) and operational instructions stored on memory(e.g., driver software) that are executed by one or more of theprocessing modules 50-1 through 50-N and/or a processing circuit withinthe particular module.

The I/O module 104 further includes one or more wireless location modems84 (e.g., global positioning satellite (GPS), Wi-Fi, angle of arrival,time difference of arrival, signal strength, dedicated wirelesslocation, etc.) and one or more wireless communication modems 86 (e.g.,a cellular network transceiver, a wireless data network transceiver, aWi-Fi transceiver, a Bluetooth transceiver, a 315 MHz transceiver, a zigbee transceiver, a 60 GHz transceiver, etc.). The I/O module 104 furtherincludes a telco interface 108 (e.g., to interface to a public switchedtelephone network), a wired local area network (LAN) 88 (e.g., optical,electrical), and a wired wide area network (WAN) 90 (e.g., optical,electrical). The I/O module 104 further includes one or more peripheraldevices (e.g., peripheral devices 1-P) and one or more universal serialbus (USB) devices (USB devices 1-U). In other embodiments, the computingdevice 100 may include more or less devices and modules than shown inthis example embodiment.

FIG. 4 is a schematic block diagram of an embodiment of the environmentsensor module 14 of the computing system 10 that includes a sensorinterface module 120 to output environment sensor information 150 basedon information communicated with a set of sensors. The set of sensorsincludes a visual sensor 122 (e.g., to the camera, 3-D camera, 360° viewcamera, a camera array, an optical spectrometer, etc.) and an audiosensor 124 (e.g., a microphone, a microphone array). The set of sensorsfurther includes a motion sensor 126 (e.g., a solid-state Gyro, avibration detector, a laser motion detector) and a position sensor 128(e.g., a Hall effect sensor, an image detector, a GPS receiver, a radarsystem).

The set of sensors further includes a scanning sensor 130 (e.g., CATscan, MRI, x-ray, ultrasound, radio scatter, particle detector, lasermeasure, further radar) and a temperature sensor 132 (e.g., thermometer,thermal coupler). The set of sensors further includes a humidity sensor134 (resistance based, capacitance based) and an altitude sensor 136(e.g., pressure based, GPS-based, laser-based).

The set of sensors further includes a biosensor 138 (e.g., enzyme,immuno, microbial) and a chemical sensor 140 (e.g., mass spectrometer,gas, polymer). The set of sensors further includes a magnetic sensor 142(e.g., Hall effect, piezo electric, coil, magnetic tunnel junction) andany generic sensor 144 (e.g., including a hybrid combination of two ormore of the other sensors).

FIG. 5A is a schematic block diagram of another embodiment of acomputing system that includes the environment model database 16, thehuman interface module 18, the instructor 26-1, the experience creationmodule 30, and the learning assets database 34 of FIG. 1. In an exampleof operation, the experience creation module 30 obtains modeledenvironment information 40 from the environment model database 16 andrenders a representation of an environment and objects of the modeledenvironment information 40 to output as instructor output information160. The human interface module 18 transforms the instructor outputinformation 160 into human output 162 for presentation to the instructor26-1. For example, the human output 162 includes a 3-D visualization andstereo audio output.

In response to the human output 162, the human interface module 18receives human input 164 from the instructor 26-1. For example, thehuman input 164 includes pointer movement information and human speechassociated with a lesson. The human interface module 18 transforms thehuman input 164 into instructor input information 166. The instructorinput information 166 includes one or more of representations ofinstructor interactions with objects within the environment and explicitevaluation information (e.g., questions to test for comprehension level,and correct answers to the questions).

Having received the instructor input information 166, the experiencecreation module 30 renders a representation of the instructor inputinformation 166 within the environment utilizing the objects of themodeled environment information 40 to produce learning asset information48 for storage in the learnings assets database 34. Subsequent access ofthe learning assets information 48 facilitates a learning experience.

FIG. 5B is a schematic block diagram of an embodiment of arepresentation of a learning experience that includes a virtual place168 and a resulting learning objective 170. A learning objectiverepresents a portion of an overall learning experience, where thelearning objective is associated with at least one major concept ofknowledge to be imparted to a learner. The major concept may includeseveral sub-concepts. The makeup of the learning objective is discussedin greater detail with reference to FIG. 6.

The virtual place 168 includes a representation of an environment (e.g.,a place) over a series of time intervals (e.g., time 0-N). Theenvironment includes a plurality of objects 24-1 through 24-N. At eachtime reference, the positions of the objects can change in accordancewith the learning experience. For example, the instructor 26-1 of FIG.5A interacts with the objects to convey a concept. The sum of thepositions of the environment and objects within the virtual place 168 iswrapped into the learning objective 170 for storage and subsequentutilization when executing the learning experience.

FIG. 6 is a schematic block diagram of another embodiment of arepresentation of a learning experience that includes a plurality ofmodules 1-N. Each module includes a set of lessons 1-N. Each lessonincludes a plurality of learning objectives 1-N. The learning experiencetypically is played from left to right where learning objectives aresequentially executed in lesson 1 of module 1 followed by learningobjectives of lesson 2 of module 1 etc.

As learners access the learning experience during execution, theordering may be accessed in different ways to suit the needs of theunique learner based on one or more of preferences, experience,previously demonstrated comprehension levels, etc. For example, aparticular learner may skip over lesson 1 of module 1 and go right tolesson 2 of module 1 when having previously demonstrated competency ofthe concepts associated with lesson 1.

Each learning objective includes indexing information, environmentinformation, asset information, instructor interaction information, andassessment information. The index information includes one or more ofcategorization information, topics list, instructor identification,author identification, identification of copyrighted materials,keywords, concept titles, prerequisites for access, and links to relatedlearning objectives.

The environment information includes one or more of structureinformation, environment model information, background information,identifiers of places, and categories of environments. The assetinformation includes one or more of object identifiers, objectinformation (e.g., modeling information), asset ownership information,asset type descriptors (e.g., 2-D, 3-D). Examples include models ofphysical objects, stored media such as videos, scans, images, digitalrepresentations of text, digital audio, and graphics.

The instructor interaction information includes representations ofinstructor annotations, actions, motions, gestures, expressions, eyemovement information, facial expression information, speech, and speechinflections. The content associated with the instructor interactioninformation includes overview information, speaker notes, actionsassociated with assessment information, (e.g., pointing to questions,revealing answers to the questions, motioning related to posingquestions) and conditional learning objective execution orderinginformation (e.g., if the learner does this then take this path,otherwise take another path).

The assessment information includes a summary of desired knowledge toimpart, specific questions for a learner, correct answers to thespecific questions, multiple-choice question sets, and scoringinformation associated with writing answers. The assessment informationfurther includes historical interactions by other learners with thelearning objective (e.g., where did previous learners look most oftenwithin the environment of the learning objective, etc.), historicalresponses to previous comprehension evaluations, and actions tofacilitate when a learner responds with a correct or incorrect answer(e.g., motion stimulus to activate upon an incorrect answer to increasea human stress level).

FIG. 7A is a schematic block diagram of another embodiment of acomputing system that includes the learning assets database 34, theexperience execution module 32, the human interface module 18, and thelearner 28-1 of FIG. 1. In an example of operation, the experienceexecution module 32 recovers learning asset information 48 from thelearning assets database 34 (e.g., in accordance with a selection by thelearner 28-1). The experience execution module 32 renders a group oflearning objectives associated with a common lesson within anenvironment utilizing objects associated with the lesson to producelearner output information 172. The learner output information 172includes a representation of a virtual place and objects that includesinstructor interactions and learner interactions from a perspective ofthe learner.

The human interface module 18 transforms the learner output information172 into human output 162 for conveyance of the learner outputinformation 172 to the learner 28-1. For example, the human interfacemodule 18 facilitates displaying a 3-D image of the virtual environmentto the learner 28-1.

The human interface module 18 transforms human input 164 from thelearner 28-1 to produce learner input information 174. The learner inputinformation 174 includes representations of learner interactions withobjects within the virtual place (e.g., answering comprehension levelevaluation questions).

The experience execution module 32 updates the representation of thevirtual place by modifying the learner output information 172 based onthe learner input information 174 so that the learner 28-1 enjoysrepresentations of interactions caused by the learner within the virtualenvironment. The experience execution module 32 evaluates the learnerinput information 174 with regards to evaluation information of thelearning objectives to evaluate a comprehension level by the learner28-1 with regards to the set of learning objectives of the lesson.

FIG. 7B is a schematic block diagram of another embodiment of arepresentation of a learning experience that includes the learningobjective 170 and the virtual place 168. In an example of operation, thelearning objective 170 is recovered from the learning assets database 34of FIG. 7A and rendered to create the virtual place 168 representationsof objects 24-1 through 24-N in the environment from time referenceszero through N. For example, a first object is the instructor 26-1 ofFIG. 5A, a second object is the learner 28-1 of FIG. 7A, and theremaining objects are associated with the learning objectives of thelesson, where the objects are manipulated in accordance with annotationsof instructions provided by the instructor 26-1.

The learner 28-1 experiences a unique viewpoint of the environment andgains knowledge from accessing (e.g., playing) the learning experience.The learner 28-1 further manipulates objects within the environment tosupport learning and assessment of comprehension of objectives of thelearning experience.

FIGS. 8A-8C are schematic block diagrams of another embodiment of acomputing system illustrating an example of creating a learningexperience. The computing system includes the environment model database16, the experience creation module 30, and the learning assets database34 of FIG. 1. The experience creation module 30 includes a learning pathmodule 180, an asset module 182, an instruction module 184, and a lessongeneration module 186.

In an example of operation, FIG. 8 A illustrates the learning pathmodule 180 determining a learning path (e.g., structure and ordering oflearning objectives to complete towards a goal such as a certificate ordegree) to include multiple modules and/or lessons. For example, thelearning path module 180 obtains learning path information 194 from thelearning assets database 34 and receives learning path structureinformation 190 and learning objective information 192 (e.g., from aninstructor) to generate updated learning path information 196.

The learning path structure information 190 includes attributes of thelearning path and the learning objective information 192 includes asummary of desired knowledge to impart. The updated learning pathinformation 196 is generated to include modifications to the learningpath information 194 in accordance with the learning path structureinformation 190 in the learning objective information 192.

The asset module 182 determines a collection of common assets for eachlesson of the learning path. For example, the asset module 182 receivessupporting asset information 198 (e.g., representation information ofobjects in the virtual space) and modeled asset information 200 from theenvironment model database 16 to produce lesson asset information 202.The modeled asset information 200 includes representations of anenvironment to support the updated learning path information 196 (e.g.,modeled places and modeled objects) and the lesson asset information 202includes a representation of the environment, learning path, theobjectives, and the desired knowledge to impart.

FIG. 8B further illustrates the example of operation where theinstruction module 184 outputs a representation of the lesson assetinformation 202 as instructor output information 160. The instructoroutput information 160 includes a representation of the environment andthe asset so far to be experienced by an instructor who is about toinput interactions with the environment to impart the desired knowledge.

The instruction module 184 receives instructor input information 166from the instructor in response to the instructor output information160. The instructor input information 166 includes interactions from theinstructor to facilitate imparting of the knowledge (e.g., instructorannotations, pointer movements, highlighting, text notes, and speech)and testing of comprehension of the knowledge (e.g., valuationinformation such as questions and correct answers). The instructionmodule 184 obtains assessment information (e.g., comprehension testpoints, questions, correct answers to the questions) for each learningobjective based on the lesson asset information 202 and producesinstruction information 204 (e.g., representation of instructorinteractions with objects within the virtual place, evaluationinformation).

FIG. 8C further illustrates the example of operation where the lessongeneration module 186 renders (e.g., as a multidimensionalrepresentation) the objects associated with each lesson (e.g., assets ofthe environment) within the environment in accordance with theinstructor interactions for the instruction portion and the assessmentportion of the learning experience. Each object is assigned a relativeposition in XYZ world space within the environment to produce the lessonrendering.

The lesson generation module 186 outputs the rendering as a lessonpackage 206 for storage in the learning assets database 34. The lessonpackage 206 includes everything required to replay the lesson for asubsequent learner (e.g., representation of the environment, theobjects, the interactions of the instructor during both the instructionand evaluation portions, questions to test comprehension, correctanswers to the questions, a scoring approach for evaluatingcomprehension, all of the learning objective information associated witheach learning objective of the lesson).

FIG. 8D is a logic diagram of an embodiment of a method for creating alearning experience within a computing system (e.g., the computingsystem 10 of FIG. 1). In particular, a method is presented inconjunction with one or more functions and features described inconjunction with FIGS. 1-7B, and also FIGS. 8A-8C. The method includesstep 220 where a processing module of one or more processing modules ofone or more computing devices within the computing system determinesupdated learning path information based on learning path information,learning path structure information, and learning objective information.For example, the processing module combines a previous learning pathwith obtained learning path structure information in accordance withlearning objective information to produce the updated learning pathinformation (i.e., specifics for a series of learning objectives of alesson).

The method continues at step 222 where the processing module determineslesson asset information based on the updated learning path information,supporting asset information, and modeled asset information. Forexample, the processing module combines assets of the supporting assetinformation (e.g., received from an instructor) with assets and a placeof the modeled asset information in accordance with the updated learningpath information to produce the lesson asset information. The processingmodule selects assets as appropriate for each learning objective (e.g.,to facilitate the imparting of knowledge based on a predeterminationand/or historical results).

The method continues at step 224 where the processing module obtainsinstructor input information. For example, the processing module outputsa representation of the lesson asset information as instructor outputinformation and captures instructor input information for each lesson inresponse to the instructor output information. Further obtain assetinformation for each learning objective (e.g., extract from theinstructor input information).

The method continues at step 226 where the processing module generatesinstruction information based on the instructor input information. Forexample, the processing module combines instructor gestures and furtherenvironment manipulations based on the assessment information to producethe instruction information.

The method continues at step 228 where the processing module renders,for each lesson, a multidimensional representation of environment andobjects of the lesson asset information utilizing the instructioninformation to produce a lesson package. For example, the processingmodule generates the multidimensional representation of the environmentthat includes the objects and the instructor interactions of theinstruction information to produce the lesson package. For instance, theprocessing module includes a 3-D rendering of a place, backgroundobjects, recorded objects, and the instructor in a relative position XYZworld space over time.

The method continues at step 230 where the processing module facilitatesstorage of the lesson package. For example, the processing moduleindexes the one or more lesson packages of the one or more lessons ofthe learning path to produce indexing information (e.g., title, author,instructor identifier, topic area, etc.). The processing module storesthe indexed lesson package as learning asset information in a learningassets database.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 8E, 8F, 8G, 8H, 8J, and 8K are schematic block diagrams of anotherembodiment of a computing system illustrating another example of amethod to create a learning experience. The embodiment includes creatinga multi-disciplined learning tool regarding a topic. Themulti-disciplined aspect of the learning tool includes both disciplinesof learning and any form/format of presentation of content regarding thetopic. For example, a first discipline includes mechanical systems, asecond discipline includes electrical systems, and a third disciplineincludes fluid systems when the topic includes operation of a combustionbased engine. The computing system includes the environment modeldatabase 16 of FIG. 1, the learning assets database 34 of FIG. 1, andthe experience creation module 30 of FIG. 1.

FIG. 8E illustrates the example of operation where the experiencecreation module 30 creates a first-pass of a first learning object 700-1for a first piece of information regarding the topic to include a firstset of knowledge bullet-points 702-1 regarding the first piece ofinformation. The creating includes utilizing guidance from an instructorand/or reusing previous knowledge bullet-points for a related topic. Forexample, the experience creation module 30 extracts the bullet-pointsfrom one or more of learning path structure information 190 and learningobjective information 192 when utilizing the guidance from theinstructor. As another example, the experience creation module 30extracts the bullet-points from learning path information 194 retrievedfrom the learning assets database 34 when utilizing previous knowledgebullet points for the related topic.

Each piece of information is to impart additional knowledge related tothe topic. The additional knowledge of the piece of information includesa characterization of learnable material by most learners in just a fewminutes. As a specific example, the first piece of information includes“4 cycle engine intake cycles” when the topic includes “how a 4 cycleengine works.”

Each of the knowledge bullet-points are to impart knowledge associatedwith the associated piece of information in a logical (e.g., sequential)and knowledge building fashion. As a specific example, the experiencecreation module 30 creates the first set of knowledge bullet-points702-1 based on instructor input to include a first bullet point “intakestroke: intake valve opens, air/fuel mixture pulled into cylinder bypiston” and a second bullet point “compression stroke: intake valvecloses, piston compresses air/fuel mixture in cylinder” when the firstpiece of information includes the “4 cycle engine intake cycles.”

FIG. 8F further illustrates the example of operation where theexperience creation module 30 creates a first-pass of a second learningobject 700-2 for a second piece of information regarding the topic toinclude a second set of knowledge bullet-points 702-2 regarding thesecond piece of information. As a specific example, the experiencecreation module 30 creates the second set of knowledge bullet-points702-2 based on the instructor input to include a first bullet point“power stroke: spark plug ignites air/fuel mixture pushing piston” and asecond bullet point “exhaust stroke: exhaust valve opens and pistonpushes exhaust out of cylinder, exhaust valve closes” when the secondpiece of information includes “4 cycle engine outtake cycles.”

FIG. 8G further illustrates the example of operation where theexperience creation module 30 obtains illustrative assets 704 based onthe first and second set of knowledge bullet-points 702-1 and 702-2. Theillustrative assets 704 depicts one or more aspects regarding the topicpertaining to the first and second pieces of information. Examples ofillustrative assets includes background environments, objects within theenvironment (e.g., things, tools), where the objects and the environmentare represented by multidimensional models (e.g., 3-D model) utilizing avariety of representation formats including video, scans, images, text,audio, graphics etc.

The obtaining of the illustrative assets 704 includes a variety ofapproaches. A first approach includes interpreting instructor inputinformation to identify the illustrative asset. For example, theexperience creation module 30 interprets instructor input information toidentify a cylinder asset.

A second approach includes identifying a first object of the first andsecond set of knowledge bullet-points as an illustrative asset. Forexample, the experience creation module 30 identifies the piston objectfrom both the first and second set of knowledge bullet-points.

A third approach includes determining the illustrative assets 704 basedon the first object of the first and second set of knowledgebullet-points. For example, the experience creation module 30 accessesthe environment model database 16 to extract information about an assetfrom one or more of supporting asset information 198 and modeled assetinformation 200 for a sparkplug when interpreting the first and secondset of knowledge bullet-points.

FIG. 8H further illustrates the example of operation where theexperience creation module 30 creates a second-pass of the firstlearning object 700-1 to further include first descriptive assets 706-1regarding the first piece of information based on the first set ofknowledge bullet-points 702-1 and the illustrative assets 704.Descriptive assets include instruction information that utilizes theillustrative asset 704 to impart knowledge and subsequently test forknowledge retention. The embodiments of the descriptive assets includesmultiple disciplines and multiple dimensions to provide improvedlearning by utilizing multiple senses of a learner. Examples of theinstruction information includes annotations, actions, motions,gestures, expressions, recorded speech, speech inflection information,review information, speaker notes, and assessment information.

The creating the second-pass of the first learning object 700-1 includesgenerating a representation of the illustrative assets 704 based on afirst knowledge bullet-point of the first set of knowledge bullet-points702-1. For example, the experience creation module 30 renders 3-D framesof a 3-D model of the cylinder, the piston, the spark plug, the intakevalve, and the exhaust valve in motion when performing the intake strokewhere the intake valve opens and the air/fuel mixture is pulled into thecylinder by the piston.

The creating of the second-pass of the first learning object 700-1further includes generating the first descriptive assets 706-1 utilizingthe representation of the illustrative assets 704. For example, theexperience creation module 30 renders 3-D frames of the 3-D models ofthe various engine parts without necessarily illustrating the first setof knowledge bullet-points 702-1.

In an embodiment where the experience creation module 30 generates therepresentation of the illustrative assets 704, the experience creationmodule 30 outputs the representation of the illustrative asset 704 asinstructor output information 160 to an instructor. For example, the 3-Dmodel of the cylinder and associated parts.

The experience creation module 30 receives instructor input information166 in response to the instructor output information 160. For example,the instructor input information 166 includes instructor annotations tohelp explain the intake stroke (e.g., instructor speech, instructorpointer motions). The experience creation module 30 interprets theinstructor input information 166 to produce the first descriptive assets706-1. For example, the renderings of the engine parts include theintake stroke as annotated by the instructor.

FIG. 8J further illustrates the example of operation where theexperience creation module 30 creates a second-pass of the secondlearning object 700-2 to further include second descriptive assets 706-2regarding the second piece of information based on the second set ofknowledge bullet-points 702-2 and the illustrative assets 704. Forexample, the experience creation module 30 creates 3-D renderings of thepower stroke and the exhaust stroke as annotated by the instructor basedon further instructor input information 166.

FIG. 8K further illustrates the example of operation where theexperience creation module 30 links the second-passes of the first andsecond learning objects 700-1 and 700-2 together to form at least aportion of the multi-disciplined learning tool. For example, theexperience creation module 30 aggregates the first learning object 700-1and the second learning object 700-2 to produce a lesson package 206 forstorage in the learning assets database 34.

In an embodiment, the linking of the second-passes of the first andsecond learning objects 700-1 and 700-2 together to form the at leastthe portion of the multi-disciplined learning tool includes generatingindex information for the second-passes of first and second learningobjects to indicate sharing of the illustrative asset 704. For example,the experience creation module 30 generates the index information toidentify the first learning object 700-1 and the second learning object700-2 as related to the same topic.

The linking further includes facilitating storage of the indexinformation and the first and second learning objects 700-1 and 700-2 inthe learning assets database 34 to enable subsequent utilization of themulti-disciplined learning tool. For example, the experience creationmodule 30 aggregates the first learning object 700-1, the secondlearning object 700-2, and the index information to produce the lessonpackage 206 for storage in the learning assets database 34.

The method described above with reference to FIGS. 8E-8K in conjunctionwith the experience creation module 30 can alternatively be performed byother modules of the computing system 10 of FIG. 1 or by other devicesincluding various embodiments of the computing entity 20 of FIG. 2A. Inaddition, at least one memory section (e.g., a computer readable memory,a non-transitory computer readable storage medium, a non-transitorycomputer readable memory organized into a first memory element, a secondmemory element, a third memory element, a fourth element section, afifth memory element, a sixth memory element, etc.) that storesoperational instructions can, when executed by one or more processingmodules of the one or more computing entities of the computing system10, cause boy one or more computing devices to perform any or all of themethod steps described above.

FIGS. 9A, 9B, 9C, 9D, and 9E are schematic block diagrams of anembodiment of a computing system illustrating an example of constructinga lesson package. The computing system includes the environment modeldatabase 16 of FIG. 1, the environment sensor module 14 of FIG. 1, theexperience creation module 30 of FIG. 1, and the learning assetsdatabase 34 of FIG. 1. The environment sensor module 14 includes themotion sensor 126 of FIG. 4 and the position sensor 128 of FIG. 4. Theexperience creation module 30 includes the learning path module 180 ofFIG. 8A, the asset module 182 of FIG. 8A, the instruction module 184 ofFIG. 8A, and the lesson generation module 186 of FIG. 8A.

FIG. 9A illustrates an example of a method of operation to construct thelesson package where, in a first step the experience creation moduleobtains lesson asset information for a lesson. For example, the learningpath module 180 recovers learning path information 194 from the learningassets database 34 and receives learning path structure information 190and learning objective information 192 from an instructor to produceupdated learning path information 196 that includes structure andlearning object information including instructor based on inputs.

The asset module 182 receives supporting asset information 198 andrecovers modeled asset information 200 from the environment modeldatabase 16 to produce the lesson asset information 202 further based onthe updated learning path information 196. The lesson asset information202 represents information of the environment to support the updatedlearning path and objects within the environment.

FIG. 9B further illustrates the method of operation to construct thelesson package where, having obtained the lesson asset information 202,in a second step the experience creation module 30 generates arepresentation of a portion of a lesson package of the lesson assetinformation 202 for an instructor 26-1. For example, the instructionmodule 184 generates instructor output information 160 based on thelesson asset information 202. The instructor output information 160includes a representation of the environment and the assets so far(e.g., start of the lesson).

FIG. 9C further illustrates the method of operation to construct thelesson package where, having generated the representation of the lessonpackage for the instructor, in a third step the experience creationmodule 30 captures instructor feedback to produce instructioninformation. For example, the instruction module 184, receivesinstructor input information 166 from the instructor 26-1 in response tothe instructor output information 160. The instructor input information166 includes a representation of instructor interactions with objectswithin the virtual environment including composite evaluationinformation (e.g., explicit questions and answers).

Having captured instructor feedback, in a fourth step the experiencecreation module captures a representation of instructor physical actionsto further produce instruction information. For example, the instructionmodule 184 receives environment sensor information 150 from theenvironment sensor module 14. The environment sensor module 14 detectsphysical manipulation of real world objects by the instructor 26-1 viathe motion sensor 126 and position sensor 128 to produce the environmentsensor information 150. The physical manipulations includes detecting atool position, detecting a pointer position, detecting where a hand is,detecting a facial expression, detecting where a finger is pointing,detecting where eyes are looking, detecting feet position, etc.

Having received the environment sensor information 150 and theinstructor input information 166, the instruction module 184 generatesinstruction information 204 based on the environment sensor information150 and the instructor input information 166. The instructioninformation 204 includes a representation of instructor interactionswith objects within the virtual environment and the composite evaluationinformation. The instruction information 204 includes a continuousstream of data.

FIG. 9D further illustrates the method of operation to construct thelesson package where, having generated the instruction information 204,in a fifth step the lesson generation module 186 generates aconsequential instructive stream 804 based on the lesson assetinformation 202 and the instruction information 204. For example, thelesson generation module 186 substitutes a portion of the lesson assetinformation 202 with an adaptive representation to produce a lessondescriptive asset 800, where a storage requirement for the lessondescriptive asset 800 is less than a storage requirement for the lessonasset information 202. The substitution includes selecting the portionof the lesson asset information 202 based on an information-amplitudelevel of the portion. The information-amplitude level includes a motionlevel, a sound level, a priority of information etc. For instance, aportion is selected that falls below a threshold A.

The selecting of the portion of the lesson asset information 202 furtherincludes selecting a portion that is below a minimuminformation-amplitude threshold over a timeframe of the portion. Forexample, a portion associated with asset renderings that aresubstantially the same as previous renderings.

The substituting of the portion of the lesson asset information 202further includes selecting an adaptive representation for the selectedportion. The adaptive representation includes a recent peak value, alast average value, a default value, a function of last value, etc. Inan instance, the lesson descriptive asset 800 is established at athreshold B default level for the timeframe and an average value overall other timeframes.

In a similar fashion to generating the lesson descriptive asset 800, thelesson generation module 186 substitutes a portion of the instructioninformation 204 with another adaptive representation to produce aninstruction descriptive asset 802, where a storage requirement for theinstruction descriptive asset 802 is less than a storage requirement forthe instruction information 204. In an instance, the instructiondescriptive asset 802 is established below a threshold D level fortimeframes (e.g., a first at zero and a second at a last value) wherethe instruction information 204 falls below a threshold level C and anaverage value over all other timeframes.

Having produced the lesson descriptive asset 800 and the instructiondescriptive asset 802, the lesson generation module 186 generates theconsequential instructive stream 804 based on the lesson descriptiveasset 800 and the instruction descriptive asset 802. The generatingincludes one or more of a simple aggregation, favoring one over theother based on a default, in accordance with a predetermined schedule,based on importance of information, and using an information-amplitudeabsolute value and/or an information-amplitude threshold. The generatingfurther includes picking neither and substituting a representation ofthem both, using a recent peak value, using a last avg value, using adefault value, and determining a function of a previous value. Forinstance, the consequential instructive stream 804 is determined as anaverage of the lesson descriptive asset 800 and the instructiondescriptive asset 802.

FIG. 9E further illustrates the method of operation to construct thelesson package where, having generated the instruction information 204,in a sixth step the experience creation module generates a lessonpackage. For example, the lesson generation module 186 generates thelesson package 206 for storage in the learning assets database 34utilizing the consequential instructive stream 804 to provide a loweredstorage capacity utilization level benefit.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 10A, 10B, 10C, and 10D are schematic block diagrams of anembodiment of a computing system illustrating an example of constructinga lesson package. The computing system includes the environment sensormodule 14 of FIG. 1, the experience creation module 30 of FIG. 1, andthe learning assets database 34 of FIG. 1. The environment sensor module14 includes the motion sensor 126 of FIG. 4 and the position sensor 128of FIG. 4. The experience creation module 30 includes the learning pathmodule 180 of FIG. 8A, the asset module 182 of FIG. 8A, the instructionmodule 184 of FIG. 8A, and the lesson generation module 186 of FIG. 8A.

FIG. 10A illustrates an example of a method of operation to constructthe lesson package where, in a first step the experience creation module30 generates a representation of a portion of lesson asset information202 for an instructor 26-1, where the portion includes time-basedcontent (e.g., a frame by frame rendering). For example, the instructionmodule 184 receives the lesson asset information 202 from the assetmodule 182 and generates instructor output information 160 based on thelesson asset information 202. The instructor output information 160includes a representation of the environment and the assets so far(e.g., video, a simulation, an animation, etc.).

FIG. 10B further illustrates the example of the method of operation toconstruct the lesson package where, having generated the representationof the portion of the lesson package for the instructor, in a secondstep the experience creation module 30 captures instructor feedback toproduce instruction information. For example, the instruction module184, receives instructor input information 166 from the instructor 26-1in response to the instructor output information 160. The instructorinput information 166 includes a representation of instructorinteractions with objects within the virtual environment includingcomposite evaluation information (e.g., explicit questions and answers).

Having captured instructor feedback, in a third step the experiencecreation module 30 captures a representation of instructor physicalactions to further produce instruction information. For example, theinstruction module 184 receives environment sensor information 150 fromthe environment sensor module 14. The environment sensor module 14detects physical manipulation of real world objects by the instructor26-1 via the motion sensor 126 and position sensor 128 to produce theenvironment sensor information 150. The physical manipulations includesdetecting a tool position, detecting a pointer position, detecting wherea hand is, detecting a facial expression, detecting where a finger ispointing, detecting where eyes are looking, detecting feet position,etc.

Having received the environment sensor information 150 and theinstructor input information 166, the instruction module 184 generatesinstruction information 204 based on the environment sensor information150 and the instructor input information 166. The instructioninformation 204 includes a representation of instructor interactionswith objects within the virtual environment and composite evaluationinformation. The instruction information 204 includes a continuousstream of data.

FIG. 10C further illustrates the example of the method of operation toconstruct the lesson package where, having generated the instructioninformation 204, in a fourth step the instruction module 184 determinesto replicate lesson asset information rendering frames (e.g., pause theimage stream to allow the instructor to make comments and annotate thepaused image). The determining includes a variety of approaches. A firstapproach includes detecting a knowledge bullet-point associated withframe of lesson asset information. A second approach includes detectinga change in instructor input information and/or environment sensorinformation. A third approach includes detecting a pause buttonactivation. A fourth approach includes determining that a maximumtimeframe without instructor annotation has expired.

Having determined to replicate the lesson asset information renderingframes, in a fifth step the instruction module 184 temporarilyreplicates lesson asset information rendering frames while generatinginstruction information rendering frames using the replicated lessonasset frames and based on instruction information. For example, theinstruction module 184 replicates the intake stroke rendering frame 2 asframes 2-4 of the instruction information 204 and adds instructorannotations. For instance, annotation “intake valve open” is added toframe 3 and “piston pulls in air/fuel mixture” is added to frame 4.

Having added the annotations, next frames of the lesson assetinformation 202 are utilized to create further frames of the instructioninformation 204. For example, the instruction module 184 generates frame5 of the instruction information 204 utilizing frame 3 of the lessonasset information 202.

FIG. 10D further illustrates the method of operation to construct thelesson package where, having generated the instruction information 204,in a sixth step the experience creation module generates a lessonpackage. For example, the lesson generation module 186 generates thelesson package 206 for storage in the learning assets database 34utilizing the instruction information 204.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 11A, 11B, 11C, and 11D are schematic block diagrams of anembodiment of a computing system illustrating an example of constructinga lesson package. The computing system includes the environment sensormodule 14 of FIG. 1, the experience creation module 30 of FIG. 1, andthe learning assets database 34 of FIG. 1. The environment sensor module14 includes the motion sensor 126 of FIG. 4 and the position sensor 128of FIG. 4. The experience creation module 30 includes the learning pathmodule 180 of FIG. 8A, the asset module 182 of FIG. 8A, the instructionmodule 184 of FIG. 8A, and the lesson generation module 186 of FIG. 8A.

FIG. 11A illustrates an example of a method of operation to constructthe lesson package where, in a first step the experience creation module30 generates a representation of a previously generated lesson packagefor a second instructor, where the lesson package includes previouslyannotated time-based content by a first instructor (e.g., a frame byframe rendering). For example, the instruction module 184 receives thelesson package 206 from the asset module 182 and generates instructoroutput information 160 based on the lesson package 206. The instructoroutput information 160 includes a representation of the environment andthe assets so far (e.g., vide, a simulation, an animation, etc.).

FIG. 11B further illustrates the example of the method of operation toconstruct the lesson package where, having generated the representationof the portion of the lesson package for the instructor, in a secondstep the experience creation module 30 captures instructor feedback fromthe second instructor to produce instruction information. For example,the instruction module 184, receives instructor input information 166from the instructor 26-2 in response to the instructor outputinformation 160. The instructor input information 166 includes arepresentation of second instructor interactions with objects within thevirtual environment including composite evaluation information (e.g.,explicit questions and answers).

Having captured instructor feedback, in a third step the experiencecreation module 30 captures a representation of instructor physicalactions to further produce instruction information. For example, theinstruction module 184 receives environment sensor information 150 fromthe environment sensor module 14. The environment sensor module 14detects physical manipulation of real world objects by the instructor26-2 via the motion sensor 126 and position sensor 128 to produce theenvironment sensor information 150. The physical manipulations includesdetecting a tool position, detecting a pointer position, detecting wherea hand is, detecting a facial expression, detecting where a finger ispointing, detecting where eyes are looking, detecting feet position,etc.

Having received the environment sensor information 150 and theinstructor input information 166, the instruction module 184 generatesinstruction information 204 based on the environment sensor information150 and the instructor input information 166. The instructioninformation 204 includes a representation of second instructorinteractions with objects within the virtual environment and compositeevaluation information. The instruction information 204 includes acontinuous stream of data.

FIG. 11C further illustrates the example of the method of operation toconstruct the lesson package where, having generated the instructioninformation 204, in a fourth step the instruction module 184 determinesto replicate lesson package rendering frames (e.g., pause the imagestream to allow the second instructor to make comments and annotate thepaused image). The determining includes a variety of approaches. A firstapproach includes identifying a portion of the lesson package withunfavorable comprehension, where the portion is affiliated with thefirst instructor. For instance, the instruction module 184 determinesthat learner comprehension associated with the engine example where thepiston pulls in the air/fuel mixture has unfavorable comprehension andrequires further explanation.

A second approach includes detecting a knowledge bullet-point associatedwith frame of lesson asset information. A third approach includesdetecting a change in second instructor input information and/orenvironment sensor information. A fourth approach includes detecting apause button activation by the second instructor. A fifth approachincludes determining that a maximum timeframe without second instructorannotation has expired.

Having determined to replicate the lesson asset information renderingframes, in a fifth step the instruction module 184 temporarilyreplicates lesson asset information rendering frames while generatinginstruction information rendering frames using the replicated lessonasset frames and based on instruction information from the secondinstructor. For example, the instruction module 184 replicates theintake stroke rendering frame 4 as frames 4-5 of the instructioninformation 204 and adds instructor annotations from the secondinstructor. For instance, annotation “valve closes before end of pistontravel” along with a circle around the intake valve is added to frame 4.

Having added the annotations, next frames of the lesson assetinformation 202 are utilized to create further frames of the instructioninformation 204. For example, the instruction module 184 generates frame6 of the instruction information 204 utilizing frame 5 of the lessonasset information 202.

FIG. 11D further illustrates the method of operation to construct thelesson package where, having generated the instruction information 204,in a sixth step the experience creation module generates an updatedlesson package. For example, the lesson generation module 186 generatesan updated lesson package 840 for storage in the learning assetsdatabase 34 utilizing the instruction information 204.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 12A, 12B, 12C, 12D, 12E, 12F, and 12G are schematic block diagramsof an embodiment of a computing system illustrating examples of creatinglesson asset information. The computing system includes the experienceexecution module 32 of FIG. 1, the learning assets database 34 of FIG.1, and the environment sensor module 14 of FIG. 1. The experienceexecution module 32 includes an environment generation module 240, aninstance experience module 290, and a learning assessment module 330.The environment sensor module 14 includes the motion sensor 126 of FIG.4 and the position sensor 120 of FIG. 4.

FIG. 12A illustrates a first example of operation of a method to createlesson asset information, where in a first step the experience executionmodule 32 generates a representation of a first portion of a lessonpackage based on a learner profile. The learner profile guides howportions of the lesson package are represented, including substitutingalternative renderings for an object (e.g., to block, to simplify, tomislead, as an “Easter egg”, to change color, to change scale, toreposition, to provide a different action).

In an example of generating the representation of the first portion ofthe lesson package, the environment generation module 240 generatesinstruction information 204 and baseline environment and objectinformation 292 based on a lesson package 206 recovered from thelearning assets database 34. The instruction information 204 includes arepresentation of instructor interactions with objects within thevirtual environment and evaluation information. The baseline environmentand object information 292 includes XYZ positioning information of eachobject within the environment for the lesson package 206.

The instance experience module 290 generates learner output information172 for the first portion of the lesson package based on the learnerprofile, the instruction information 204 and the baseline environmentand object information 292. The learner output information 172 includesa representation of a virtual place with objects, instructorinteractions, and learner interactions from a perspective of thelearner. The learner output information 172 further includesrepresentations of the instruction information (e.g., instructorannotations).

The generating of the learner output information 172 based on thelearner profile further includes utilizing one or more of a defaultsetting for a view of one or more renderings (e.g., a perspective, ascale, a position, etc.), utilizing a previous setting, and inaccordance with a security level and/or a permissions level etc. (e.g.,blacking out or enabling viewing of an element of a rendering that isproprietary or associated with a high level of security access).

FIG. 12B further illustrates the example of operation the method tocreate lesson asset information where, having generated therepresentation of the first portion of the lesson package in accordancewith the learner profile, in a second step the experience executionmodule 32 captures learner feedback to provide an assessment. Forexample, the instance experience module 290 generates learnerinteraction information 332 based on assessment information 252 andlearner input information 174. The learner input information 174includes session control information, answer object manipulation, anddirect answer input (e.g., text, speech). The assessment information 252includes an updated representation of the assessment information basedon learner input information, functionality and/or time correlations offurther learner input information to the further learner outputinformation to produce correlated assessment learner input information(e.g., time stamped and manipulated answer information).

Having captured the learner feedback, in a third step the experienceexecution module 32 captures a representation of learner physicalactions to further provide the assessment. For example, the learningassessment module 330 receives environment sensor information 150 fromthe environment sensor module 14 based on inputs from the learner 28-1to the motion sensor 126 and the position sensor 128. For instance, theenvironment sensor module 14 generates the environment sensorinformation 150 based on detecting physical manipulation of real-worldobjects by the student (e.g., tool position, a bat position, a golf clubposition, etc.).

FIG. 12C further illustrates the example of operation of the method tocreate lesson asset information where, having captured representationsof the learner physical actions, in a fourth step the experienceexecution module 32 analyzes learner interaction information 332 andenvironment sensor information 150 in light of assessment information252 to produce learning assessment results information 334. For example,the learning assessment module 330 analyzes the environment sensorinformation 150 to interpret physical actions of the learner 28-1 andcompares the physical actions to specifications of the assessmentinformation 252 to produce the learning assessment results information334.

The learning assessment results information 334 includes one or more ofa learner identity, a learning object identifier, a lesson identifier,and raw learner interaction information (e.g., a timestamp recording ofall learner interactions like points, speech, input text, settings,viewpoints, etc.). The learning assessment results information 334further includes summarized learner interaction information (e.g.,average, mins, maxes of raw interaction information, time spent lookingat each view of a learning object, how fast answers are provided, numberof wrong answers, number of right answers, etc.).

Having produced the learning assessment results information, in a fifthstep the experience execution module 32 updates the learner profile andthe learner output information 172 based on the learning assessmentresults information. For example, the instance experience module 290determines to occlude viewing of a portion of the previous learneroutput information 172 when the learning assessment results information334 indicates a disinterest by the learner 28-1 for that portion.

FIG. 12D further illustrates step 5 of the method to create lesson assetinformation where, the instance experience module 290 updates thelearner profile and learner output information 172 based on the learningassessment results information 334. In an example of updating thelearner output information 172, the instance experience module 290determines that the learner is not associated with a sufficient securitylevel to view a proprietary aspect of a rendering of an engine partbased on the learner profile. In an instance, the lesson packagerendering frame 3 is updated to remove the proprietary aspect and tochange associated annotation (e.g., from “proprietary piston head shape”to “piston head”) to produce a learner output rendering frame 3. In asimilar fashion, the lesson package rendering frame 4 is updated toproduce the learner output rendering frame 4 to suppress proprietaryaspects.

In another example, the learner output rendering is updated to include amore complicated representation of and associated lesson packagerendering frame when the learner profile indicates that a security levelassociated with the learner is above a minimum threshold level.

FIGS. 12E-12G are schematic block diagrams of the embodiment of thecomputing system illustrating examples of creating lesson assetinformation. The computing system further includes human interfacemodules 18-1 and 18-2 and illustrates a second example of operation ofthe method to create the lesson asset information. The human interfacemodules 18-1 and 18-2 are implemented utilizing the human interfacemodule 18 of FIG. 1.

FIG. 12E illustrates a first step of the second example of operation ofthe method to create lesson asset information, where the experienceexecution module 32 obtains a first learner approach associated with afirst learner 28-1 of a set of learners (e.g., 28-1 and 28-2). The firstlearner approach is associated with what knowledge and how to portraythe knowledge to the first learner. The approach constrains access toknowledge when the knowledge requires a particularauthorization/security level. The approach provides access to knowledgewhen the learner has sufficient authorization. The approach enhancesaccess to knowledge when the learner is equipped to learn more. Theapproach curtails access to knowledge when the learner is less equippedto learn.

The obtaining the first learner approach associated with the firstlearner includes a variety of alternatives. A first alternative includesthe first learner approach to exclude utilization of a constrainedknowledge bullet-point from inclusion in the first set of knowledgebullet-points and the second set of knowledge bullet-points. Forexample, the experience execution module 32 excludes utilization of abullet point that references a proprietary piston head of an engine. Asecond alternative includes establishing the first learner approach tomodify the constrained knowledge bullet-point to produce a modifiedknowledge bullet-point for inclusion in at least one of the first set ofknowledge bullet-points and the second set of knowledge bullet-points.For example, the experience execution module 32 modifies the bulletpoint associated with the proprietary piston head to that of a standardpiston head.

A third alternative includes establishing the first learner approach toinclude utilization of a first expansion knowledge bullet-point in thefirst set of knowledge bullet-points for an expansion first piece ofinformation associated with the first piece of information regarding thetopic. For example, the experience execution module 32 utilizes a firstexpansion knowledge bullet-point associated with a particular alloy ofthe piston head for the expansion first piece of information associatedwith composition of parts of the engine.

A fourth alternative includes establishing the first learner approach toinclude utilization of a second expansion knowledge bullet-point in thesecond set of knowledge bullet-points for an expansion second piece ofinformation associated with the second piece of information regardingthe topic. For example, the experience execution module 32 utilizes asecond expansion knowledge bullet-point associated with velocity of fuelbeing drawn into the cylinder.

A fifth alternative includes establishing the first learner approach toexclude utilization of a constrained asset as an illustrative asset 704.For example, experience execution module 32 excludes utilization of arendering of an asset associated with the cylinder head of the enginewhen the cylinder head is a proprietary design and the first learner isnot associated with access to the proprietary design.

A sixth alternative includes establishing the first learner approach tomodify the constrained asset to produce a modified asset for inclusionas the illustrative asset 704. For example, the experience executionmodule 32 modifies the rendering of the proprietary cylinder head tothat of the standard cylinder head for inclusion as the illustrativeasset 704.

Having obtained the first learner approach, in a second step of thesecond example method of operation of creating lesson asset information,the experience execution module 32 creates first lesson assetinformation 202-1 regarding the topic for the first learner based on thefirst learner approach associated with the first learner. The firstlesson asset information 202-1 includes a first learning object 700-1and a second learning object 700-2. The first learning object 700-1includes a first set of knowledge bullet-points 702-1 for a first pieceof information regarding the topic (e.g., piston movement to draw in anair/fuel mixture through the intake valve into the cylinder). The secondlearning object 700-2 includes a second set of knowledge bullet-points702-2 for a second piece of information regarding the topic (e.g.,further movement of the piston to draw further air/fuel mixture inthrough the intake valve further into the cylinder).

The first learning object 700-1 and the second learning object 700-2further includes the illustrative asset 704 that depicts an aspectregarding the topic pertaining to the first and second pieces ofinformation. For example, representations of assets of the engine todemonstrate engine operation including a cylinder, a standard piston, aspark plug, and intake valve, and exhaust valve, and a connecting rod.

The first learning object 700-1 further includes one or more firstdescriptive assets 706-1 regarding the first piece of information basedon the first set of knowledge bullet-points 702-1 and the illustrativeasset 704. For example, rendering representations of assets of theengine to demonstrate the operation during an intake cycle. The secondlearning object 700-2 further includes one or more second descriptiveassets 706-2 regarding the second piece of information based on thesecond set of knowledge bullet-points 702-2 and the illustrative asset704. For example, further rendering representations of the assets of theengine demonstrating the operation during later stages of the intakecycle.

The creating the first lesson asset information 202-1 regarding thetopic for the first learner based on the first learner approachassociated with the first learner includes a series of sub-steps. Any ofthe sub-steps may include extracting information from lesson package206-1 retrieved from the learning assets database 34. A first sub-stepincludes obtaining the illustrative asset 704 based on the first learnerapproach. For example, the experience execution module 32 generates arendering of a standard piston head when the first learner approachcurtails access to the proprietary nature of the piston head.

A second sub-step includes obtaining the first set of knowledgebullet-points 702-1 for the first piece of information regarding thetopic based on the first learner approach. For example, the experienceexecution module 32 modifies a bullet point of the proprietary pistonhead to that of the standard piston head.

A third sub-step includes generating the first descriptive assets 706-1regarding the first piece of information based on the first set ofknowledge bullet-points 702-1 and the illustrative asset 704. Forexample, the experience execution module 32 renders representations ofthe standard piston head within the cylinder.

A fourth sub-step includes obtaining the second set of knowledgebullet-points for the second piece of information regarding the topicbased on the first learner approach. For example, the experienceexecution module 32 modifies a bullet point associated with fuel drawnin more efficiently for the proprietary cylinder head to that of justfuel being drawn in for the standard piston head.

A fifth sub-step includes generating the second descriptive assets 706-2regarding the second piece of information based on the second set ofknowledge bullet-points 702-2 and the illustrative asset 704. Forexample, the experience execution module 32 represents representationsof the standard piston head drawing fuel in within the cylinder. A sixthsub-step includes generating the first lesson asset information 202-1 toinclude the first descriptive assets 706-1 and the second descriptiveassets 706-2.

FIG. 12F illustrates a third step of the second example of operation ofthe method to create lesson asset information, where, having created thefirst lesson asset information 202-1, the experience execution module 32obtains, as previously discussed for the first learner, a second learnerapproach associated with the second learner 28-2 of the set of learners.For example, the second learner approach is obtained such that thesecond learner 28-2 has full access to knowledge associated with theproprietary cylinder head and its ability to more efficiently draw fuelinto the cylinder.

Having obtained the second learner approach, a fourth step of the methodof operation of the second example of creating lesson asset informationincludes the experience execution module 32 creating second lesson assetinformation 202-2 regarding the topic for the second learner based onthe second learner approach associated with the second learner. Thesecond learner approach is different than the first learner approach inthe example.

The second lesson asset information 202-2 includes a third learningobject 700-3 and a fourth learning object 700-4. The third learningobject 700-3 includes a third set of knowledge bullet-points 702-3 forthe first piece of information regarding the topic. The fourth learningobject 700-4 includes a fourth set of knowledge bullet-points 702-4 forthe second piece of information regarding the topic.

Third learning object 700-3 and the fourth learning object 700-4 furtherincludes the illustrative asset 704. The third learning object 700-3further includes at least one third descriptive asset 706-3 regardingthe first piece of information based on the third set of knowledgebullet-points 702-3 and the illustrative asset 704. For example, theexperience execution module 32 provides a rendering of the proprietarypiston head shape.

The fourth learning object 700-4 further includes at least one fourthdescriptive asset 706-4 regarding the second piece of information basedon the fourth set of knowledge bullet-points 702-4 and the illustrativeasset 704. For example, the experience execution module 32 provides therendering of the proprietary piston head shape and a note to indicatethat fuel is drawn and more efficiently when the second learner 28-2 isauthorized to gain knowledge associated with the proprietary pistonhead.

FIG. 12G illustrates a fifth step of the second example of operation ofthe method to create lesson asset information, where, having created thesecond lesson asset information, the experience execution module 32sends the first lesson asset information 202-1 to a first learnercomputing entity associated with the first learner. For example, theexperience execution module 32 outputs a representation (e.g., a seriesof renderings as a video) of the first descriptive assets 706-1 and thesecond descriptive assets 706-2 of the first lesson asset information202-1 to the human interface module 18-1. The human interface module18-1 interprets the first lesson asset information 202-1 to generatehuman output 162-1 for the learner 28-1 (e.g., three-dimensionalrepresentation).

Having output the first lesson asset information 202-1 to the firstlearner, a sixth step of the second example of operation of the methodto create lesson asset information includes the experience executionmodule 32 sending the second lesson asset information 202-2 to a secondlearner computing entity associated with the second learner. Forexample, the experience execution module 32 outputs a representation(e.g., a series of renderings as a video) of the third descriptiveassets 706-3 and the fourth descriptive assets 706-4 of the secondlesson asset information 202-2 to the human interface module 18-2. Thehuman interface module 18-2 interprets the second lesson assetinformation 202-2 to generate human output 162-2 for the learner 28-2(e.g., three-dimensional representation).

The methods described above in conjunction with the processing modulecan alternatively be performed by other modules of the computing system10 of FIG. 1 or by other devices. In addition, at least one memorysection (e.g., a computer readable memory, a non-transitory computerreadable storage medium, a non-transitory computer readable memoryorganized into a first memory element, a second memory element, a thirdmemory element, a fourth element section, a fifth memory element, asixth memory element, etc.) that stores operational instructions can,when executed by one or more processing modules of the one or morecomputing devices of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 13A, 13B, 13C, and 13D are schematic block diagrams of anembodiment of a computing system illustrating an example of updating alesson package. The computing system includes the experience creationmodule 30 of FIG. 1, the experience execution module 32 of FIG. 1, andthe learning assets database 34 of FIG. 1.

FIG. 13A illustrates an example of a method of operation to update thelesson package where, in a first step the experience creation module 30obtains a lesson package that includes a set of learning objects. In afirst example, the experience creation module 30 generates one or moreof the learning objects of the set of learning objects. In a secondexample, the experience execution module 32 extracts the set of learningobjects from a lesson package 206 recovered from the learning assetsdatabase 34. For instance, the experience execution module 32 extractslearning objects 880-1 through 880-2 from the lesson package 206. Thelearning object 880-1 includes first descriptive asset 756-1 and firstknowledge assessment assets 762-1. A Learning path for the learner 28-1includes execution of the learning object 880-1 followed by theexecution of learning object 880-2.

FIG. 13B further illustrates the example of the method of operation toupdate the lesson package where, having obtained the lesson package 206,a second step includes the experience execution module 32, for eachlearning object of the lesson package, obtaining learning assessmentresults information based on utilization of the lesson package. Forexample, the experience execution module 32 issues learner outputinformation 172 to the learner 28-1 in accordance with the firstdescriptive asset 756-1 of the learning object 880-1 and receiveslearner input information 174 from the learner 28-1 in response. Theexperience execution module 32 evaluates the learner input information174 in accordance with the first knowledge assessment assets 762-1 toproduce first assessment results 764-1. In a similar fashion, theexperience execution module 32 produces second assessment results 764-2of the learning object 880-2. The experience execution module 32facilitates storage of the assessment results in the learning assetsdatabase 34.

FIG. 13C further illustrates the example of the method of operation toupdate the lesson package where, having produced the learning assessmentresults, a third step includes, for each learning object of the lessonpackage, the experience creation module 30 identifying enhancements tothe descriptive assets and/or their use to produce updated descriptiveassets of an updated lesson package based on the corresponding learningassessment results information. For example, the experience creationmodule 30 recovers learning assessment results information 334 and thelesson package 206 from the learning assets database 34. Theenhancements includes one or more of creating a new illustrative asset,modifying a descriptive asset, and reordering learning object executionin accordance with an update to the learning path.

As a specific example to the identification of the enhancements, theexperience creation module 30 determines an update to the learningobject when wrong answers related to the learning object occur moreoften than a maximum incorrect answer threshold level. The update to thelearning object includes one or more of a new version, different view,taking more time on a particular object, etc. As another specificexample to the identification of the enhancements, the experiencecreation module 30 determines the update to the object when correctanswers related to the learning object occur more often than a maximumcorrect answer threshold level.

In an instance of the updating, the experience creation module 30identifies the enhancements for the second descriptive asset 756-2 toproduce updated second descriptive assets 766-2 when too many incorrectanswers are detected. The updated second descriptive asset 766-2addresses a more effective conveyance of desired knowledge to impartwith the learner 28-1 and/or another learner.

FIG. 13D further illustrates the example of the method of operation toupdate the lesson package where, having the identified enhancements tothe descriptive assets, a fourth step includes, for each learning objectof the lesson package, the experience creation module 30 identifyingenhancements to the assessment assets of an updated lesson package toproduce updated assessment assets based on the corresponding learningassessment results information and updated descriptive assets. Forexample, the experience creation module 30. The enhancements includesone or more of forming a new question, breaking down a previous questioninto one or more step-wise questions, providing a different view, takingmore time on an object, etc.

As a specific example to the identification of the enhancements to theassessment assets, the experience creation module 30 determines anupdate to the assessment assets when wrong answers related to thelearning object occur more often than the maximum incorrect answerthreshold level. The update to the assessment assets includes one ormore of a adding a new question, breaking down a previous question intomore step-wise questions, providing a different view, taking more timeon a particular object, etc. As another specific example to theidentification of the enhancements, the experience creation module 30determines the update to the assessment assets when correct answersrelated to the learning object occur more often than the maximum correctanswer threshold level. The update to the assessment assets includes oneor more of adding new more difficult questions, consolidating step-wisequestions, providing a different view, and taking less time on aparticular object.

In an instance of the updating, the experience creation module 30identifies the enhancements for the second knowledge assessment assets762-2 to produce updated second knowledge assessment assets 768-2 whentoo many incorrect answers are detected. The updated second knowledgeassessment assets 768-2 addresses a more effective assessment knowledgeretention with the learner 28-1 and/or another learner.

Having identified the enhancements to the assessment assets, a fifthstep of the example method includes facilitating storing the updatedlesson package for a subsequent enhanced use. For example, theexperience creation module 30 generates updated lesson package 810utilizing the set of learning objects with various updated descriptiveassets and knowledge assessment assets and sends the updated lessonpackage 810 two the learning assets database 34 for storage.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 14A, 14B, 14C, and 14D are schematic block diagrams of anembodiment of a computing system illustrating an example of executing alesson package. The computing system includes the experience executionmodule 32 of FIG. 1 and the learning assets database 34 of FIG. 1.

FIG. 14A illustrates an example of a method of operation to execute thelesson package where, in a first step the experience execution module 32obtains a lesson package that includes a plurality of sets of learningobjects corresponding to permutations of learning paths. The permutationof learning paths corresponds to alternative branches of learning pathas time proceeds during the execution of delivering the learningpackage. A benefit is provided where, during the execution, a preferredalternative branch is utilized to provide a better experience for thelearner 28-1.

As an example of the obtaining of the lesson package, the experienceexecution module 32 extracts the plurality of sets of learning objectsfrom a lesson package 206 recovered from the learning assets database34. For instance, the experience execution module 32 extracts learningobjects 880-1, 880-2A, 880-2B, 880-3 from the lesson package 206. Thefirst set of learning objects includes the learning object 880-1, thesecond set of learning objects includes the learning objects 880-2A and880-2B, and the third set of learning objects includes a learning object880-3. The learning object 880-1 includes first descriptive asset 756-1and first knowledge assessment assets 762-1. An embodiment, all of thelearning objects share common illustrative assets. An embodiment of aparticular permutation of learning paths for the learner 28-1 includesexecution of the learning object 880-1 followed by the execution of oneof learning object 880-2A and 880-2B followed by the execution oflearning object 880-3.

FIG. 14B further illustrates the example of the method of operation toexecute the lesson package where, having obtained the lesson package, ina second step the experience execution module 32 generates arepresentation of a first learning object of the first set of learningobjects of the plurality of sets of learning objects. For example, theexperience execution module 32 issues learner output information 172 tothe learner 28-1 based on the first descriptive assets 756-1.

FIG. 14C further illustrates the example of the method of operation toexecute the lesson package where, having generated the representation ofthe first learning object, in a third step the experience executionmodule 32 generates a first assessment of the first learning objectbased on learner input information in response to a representation offirst knowledge assessment assets. For example, the experience executionmodule 32 analyzes learner input information 174 from the learner 28-1in response to learner output information 172 based on first knowledgeassessment assets 762-1 to produce first assessment results 764-1.

FIG. 14D further illustrates the example of the method of operation toexecute the lesson package where, having generated the first assessmentof the first learning object, in a fourth step the experience executionmodule 32 selects a learning object of a second set of learning objectsof the plurality of sets of learning objects based on the firstassessment of the first learning object. The selecting includesdetermining to do one of staying on a predetermined learning path of thepermutations of learning paths, deviating from the learning path to alearning object associated with more information when determining thatmore information is required, deviating from the learning path toanother learning object associated with less information whendetermining that less information is required, and looping back to thefirst learning object when the first assessment results 764-1 compareunfavorably to a minimum comprehension threshold level.

As an example of the selecting of the learning object of the second setof learning objects, the experience execution module 32 selects thelearning object 880-2B to utilize the second descriptive asset 756-2Bwhen the second descriptive asset 756-2B is determined to be moreeffective than the selecting of the second descriptive asset 756-2A ofthe other candidate learning object of the second set of learningobjects. In an embodiment, the other candidate learning objects of thesecond set of learning objects may be selected in a subsequent step.

Having selected the learning object of the second set of learningobjects, in a fifth step the experience execution module 32 generates arepresentation of the selected learning object of the second set oflearning objects. For example, the experience execution module 32 issueslearner output information 172 to the learner 28-1 based on the seconddescriptive asset 756-2B.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 15A, 15B, and 15C are schematic block diagrams of an embodiment ofa computing system illustrating an example of creating learning outputinformation. The computing system includes the experience executionmodule 32 of FIG. 1 and the learning assets database 34 of FIG. 1. Theexperience execution module 32 includes an environment generation module240, an instance experience module 290, and a learning assessment module330. The environment sensor module 14 includes the motion sensor 126 ofFIG. 4 and the position sensor 120 of FIG. 4.

FIG. 15A illustrates an example of operation of a method to create thelearning output information, where in a first step the experienceexecution module 32 generates a representation of a first portion of alesson package that includes a learning path of a sequence of learningobjects. For example, the environment generation module 240 generatesassessment information 252, instruction information 204, baselineenvironment and object information 292 based on the lesson package 206recovered from the learning assets database 34. The instance experiencemodule 290 selects a learning object from the first portion of thelesson package and issues learner output 172 to the learner 28-1 as arepresentation of the first portion of the lesson package. The issuingof the learner output 172 includes rendering assets of the learningobject in accordance with the instruction information 204 and thebaseline environment and object information 292.

Having generated the representation of the first portion of the lessonpackage, in a second step the experience execution module 32 captureslearner input to produce learner interaction information. For example,the instance experience module 290 generates learner interactioninformation 332 based on learner input information 174 received from thelearner 28-1 and the instruction information 204.

Having produced the learner interaction information 332, in a third stepthe experience execution module 32 analyzes the learner interactioninformation to produce learning assessment results information. Forexample, the learning assessment module 330 issues learning assessmentresults information 334 based on the learner interaction information 332and in accordance with the assessment information 252.

FIG. 15B further illustrates the example of operation of the method tocreate the learning output information, where having produced thelearning assessment results information 334, in a fourth step theexperience execution module 32 selects a diversion universal resourcelocator (URL) based on the learning assessment results information toupdate the learning path. The selecting is based on one or more of aninstructor set pause and divert point in the learning path as forcedmore optional, and assessment determines supplemental information isrequired to enhance an experience and/or improve learning comprehension(e.g., the learner needs help), and the learner 28-1 and invokes arequest to learn more than what the current learning path offers. Forexample, the instance experience module 290 selects a diversion URL fromdiversion URL information 846 recovered from the learning assetsdatabase 34 for the lesson package when the learning path includes aninstructor set diversion point to pause playback of the lesson packagealong the learning path to allow the learner 28-1 to access supplementalinformation at the diversion URL.

Having selected the diversion URL, in a fifth step the experienceexecution module pauses the learning path and diverts the representationof the lesson package to the selected diversion URL. For example, theinstance experience module 290 starts replicating rendering frames of acurrent learning object while adding a representation of contentaccessed via the diversion URL information 846 to the learner outputinformation 172 to the learner 28-1.

FIG. 15C illustrates an instance of the example of operation of themethod to create the learning output information, where the instanceexperience module 290 pauses the learning path and diverts to theselected diversion URL. In the instance, rendering frame 3 of the lessonpackage (e.g., rendered from the baseline environment and objectinformation 292 and the instruction information 204) is paused startingwith rendering frame 3 of the learner output information 172.

Having paused the rendering of the lesson package, the instanceexperience module 290 renders a representation of a portion of thediversion URL information 846 (e.g., www.intake valves1.com) and outputsthe rendering(s) as further rendering frames 4-100 of the learner outputinformation 172. The outputting further includes interactivity with thelearner to access associated content options of the diversion URLinformation 846 (e.g., further webpages in response to clicks).

While diverting to the selected diversion URL, the instance experiencemodule 290 determines to halt the pause and return to playing of furtherportions of the lesson package. The determining may be based on one ormore of a detecting that a predetermined time frame has expired,detecting an input from the learner, and detecting that no furthercontent is available at the diversion URL information 846. For example,the instance experience module 290 determines that the learner hasactivated a resume function.

Having determined to return to the plane of the further portions of thelesson package, the instance experience module 290 renders furtherframes of the learner output information 172 based on the lessonpackage. For example, the instance experience module 290 providesrendering frame 4 of the lesson package as a next rendering frame of thelearner output information 172 (e.g., rendering frame 101). The methoddescribed above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 16A, 16B, 16C, and 16D are schematic block diagrams of anembodiment of a computing system illustrating an example of optimizinglearning. The computing system includes the experience execution module32 of FIG. 1, the learning assets database 34 of FIG. 1, and theenvironment sensor module 14 of FIG. 1. The experience execution module32 includes the environment generation module 240, the instanceexperience module 290, and the learning assessment module 330. Theenvironment sensor module 14 includes the motion sensor 126 of FIG. 4and the position sensor 120 of FIG. 4.

FIG. 16A illustrates an example of operation of a method to optimizelearning, where in a first step the experience execution module 32generates a representation of a first portion of a lesson package thatincludes a request for the learner to perform a physical activity. Forexample, the environment generation module 240 generates instructioninformation 204 and baseline environment and object information 292based on the lesson package 206 recovered from the learning assetsdatabase 34. The instance experience module 290 selects a learningobject from the first portion of the lesson package and issues learneroutput information 172 to the learner 28-1 as a representation of thefirst portion of the lesson package, where the learner outputinformation 172 includes the request for the learner to perform thephysical activity. The issuing of the learner output information 172includes rendering assets of the learning object in accordance with theinstruction information 204 and the baseline environment and objectinformation 292.

FIG. 16B further illustrates the example of operation of the method tooptimize learning, where, having generated the representation of thefirst portion of the lesson package that includes the request to performthe physical activity, in a second step the experience execution module32 captures learner input to produce learner interaction information.For example, the instance experience module 290 generates learnerinteraction information 332 based on learner input information 174received from the learner 28-1 and the instruction information 204.

Having produced the learner interaction information 332, in a third stepthe experience execution module 32 captures a representation of learnerphysical actions to produce environment sensor information. For example,the learning assessment module 330 receives environment sensorinformation 150 from the environment sensor module 14, where theenvironment sensor information 150 represents the learner physicalactions.

In a fourth step of the example of operation of the method to optimizelearning, the experience execution module 32 analyzes the learnerinteraction information and the representation of the learner physicalactions to produce learning assessment results information to producelearning assessment results information. For example, the learningassessment module 330 generates learning assessment results information334 based on the learner interaction information 332, the environmentsensor information 150, and in accordance with assessment information252 obtained from the environment generation module 240. The analyzingincludes verifying the physical actions, asking questions while thelearner 28-1 is under physical stress, and assessing answers from thelearner 28-1 while in the physically stressed condition.

FIG. 16C further illustrates the example of operation of the method tooptimize learning, where the instance experience module 290 analyzes thelearning assessment results information 334 with regards to firstknowledge assessment assets 762-1 of a learning object 880-1 to producefirst assessment results 764-1. The learning object 880-1 is a firstlearning object of a first set of learning objects of a plurality ofsets of learning objects, where a second set of learning objectsincludes learning object 880-2A and learning object 880-2B, and where athird set of learning objects includes learning object 880-3. In anexample of the analyzing, the instance experience module 290 analyzesthe assessment of the learners physical activities with regards to thefirst knowledge assessment assets to determine whether learning whileperforming the physical activity is favorable.

FIG. 16D further illustrates the example of operation of the method tooptimize learning, where, having produced the first assessment results,when the instance experience module 290 determines that the firstassessment results are unfavorable, in a sixth step the instanceexperience module 290 selects an abatement for a next portion of thelesson package. The selecting includes one of staying on a predeterminedlearning path of a permutation of a sequence of learning objects,deviating from the learning path when determining that the performanceunder the physical stress is unfavorable (e.g., too many incorrectanswers, etc.), and looping back to the first learning object. Forexample, the instance experience module 290 selects learning object880-2A rather than learning object 880-2B of the current learning pathwhen poor performance under physical stresses detected. The instanceexperience module 290 issues learner output information 172 to thelearner 28-1 based on second descriptive asset 756-2A.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 17A, 17B, 17C, and 17D are schematic block diagrams of anembodiment of a computing system illustrating an example of optimizinglearning. The computing system includes the experience execution module32 of FIG. 1, the learning assets database 34 of FIG. 1, and theenvironment sensor module 14. The experience execution module 32includes the environment generation module 240, the instance experiencemodule 290, and the learning assessment module 330. The environmentsensor module 14 includes various sensors to detect physical activitiesand stress of the learner 28-1.

FIG. 17A illustrates an example of operation of a method to optimizelearning, where in a first step the experience execution module 32generates a representation of a first portion of a lesson package thatincludes elements to induce learner stress. The lesson package includesa default learning path of a sequence of execution of learning objects.For example, the environment generation module 240 generates instructioninformation 204 and baseline environment and object information 292based on the lesson package 206 recovered from the learning assetsdatabase 34. The instance experience module 290 selects a learningobject from the learning path of the first portion of the lesson packageand issues enhanced learner output information 860 to the learner 28-1as the representation of the first portion of the lesson package. Theenhanced learner output information 860 includes a rendering of thelearning object with modifications to induce the learner stress (e.g.,modified visual rendering, modified background noises, modified volumelevels, etc.). The issuing of the enhanced learner output information860 further includes rendering assets of the learning object inaccordance with the instruction information 204 and the baselineenvironment and object information 292.

FIG. 17B further illustrates the example of operation of the method tooptimize learning, where in a second step, while outputting the enhancedlearner output information to the learner, experience execution module32 captures learner input to produce learner interaction information.For example, the instance experience module 290 receives learner inputinformation 174 from the learner 28-1 in response to the enhancedlearner output information 860. The instance experience module 290analyzes the learner input information 174 in accordance with assessmentinformation 252 to create learner interaction information 332.

While outputting the enhanced learner output information to the learner,in a third step, the experience execution module 32 captures environmentsensor information representing learner physical aspects. For example,the learning assessment module 330 receives environment sensorinformation 150 from the environment sensor module 14, where theenvironment sensor module 14 utilizes one or more sensors to detect thelearner physical aspects of the learner 28-1. The physical aspectsincludes a physical condition of the learner and physical movements ofthe learner.

FIG. 17C further illustrates the example of operation of the method tooptimize learning, where having received the environment sensorinformation and generated the learner interaction information, in afourth step the experience execution module 32 analyzes the learnerinteraction information and the environmental sensor information basedon the assessment information to produce learning asset resultsinformation. For example, the learning assessment module 330 analyzesthe learner interaction information 332 and the environment sensorinformation 150 based on the assessment information 252 to producelearning assessment results information 334, where the learningassessment results information is correlated to the learner physicalaspects. For instance, the learning assessment results information 334indicates the effects of different levels of stress on answercorrectness.

FIG. 17D further illustrates the example of operation of the method tooptimize learning, where having produced the learning assessment resultsinformation, in a fifth step the experience execution module 32 selectsan adaptation of a portion of the lesson package in accordance with thelearning assessment results information. The adaptation includesre-selecting the first portion (e.g., a redo) with or withoutenhancement for a next learner, selecting a different portion (e.g., anext learning object along the sequence of the learning path) for thesame learner, and selecting a different portion associated with anotherlearning object along a different sequence of a variation to thelearning path. The adaptation further includes providing an updatedassessment question, changing the pace (e.g., slower when an assessmentis unfavorable, faster when an assessment is favorable) of execution ofthe learning objects along the learning path.

Having selected the adaptation, in a sixth step the experience executionmodule 32 outputs a representation of the adaptation of the portion ofthe lesson package (e.g., to the same or a next learner). For example,the instance experience module 290 selects a different learning objectfrom an alternative learning path, generates enhanced learner outputinformation 860 based on assets of the different learning object, andoutputs the enhanced learning output information 860 to the learner28-1.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 18A, 18B, 18C, and 18D are schematic block diagrams of anembodiment of a computing system illustrating an example of optimizinglearning. The computing system includes the experience execution module32 of FIG. 1, the learning assets database 34 of FIG. 1, and theenvironment sensor module 14. The experience execution module 32includes the environment generation module 240, the instance experiencemodule 290, and the learning assessment module 330. The environmentsensor module 14 includes various sensors to detect physical activitiesand stress of the learner 28-1.

FIG. 18A illustrates an example of operation of a method to optimizelearning, where in a first step the experience execution module 32generates a representation of a first portion of a lesson package, wherea subsequent learner response is expected to identify the learner. Thelesson package includes a default learning path of a sequence ofexecution of learning objects. For example, the environment generationmodule 240 generates instruction information 204 and baselineenvironment and object information 292 based on the lesson package 206recovered from the learning assets database 34. The instance experiencemodule 290 selects a learning object from the learning path of the firstportion of the lesson package and issues learner output information 172to the learner 28-1 as the representation of the first portion of thelesson package. The issuing of the learner output information 172includes rendering assets of the learning object in accordance with theinstruction information 204 and the baseline environment and objectinformation 292. In an embodiment, the instance experience module 290generates the learner output information 172 to include elements torequest the learner to perform physical activities that induce stress tohelp identify the learner.

FIG. 18B further illustrates the example of operation of the method tooptimize learning, where in a second step, while outputting the enhancedlearner output information to the learner, experience execution module32 captures learner input to produce learner interaction information.For example, the instance experience module 290 receives learner inputinformation 174 from the learner 28-1 in response to the learner outputinformation 172. The instance experience module 290 analyzes the learnerinput information 174 in accordance with assessment information 252 tocreate learner interaction information 332.

While outputting the enhanced learner output information to the learner,in a third step, the experience execution module 32 captures environmentsensor information representing learner physical aspects. For example,the learning assessment module 330 receives environment sensorinformation 150 from the environment sensor module 14, where theenvironment sensor module 14 utilizes one or more sensors to detect thelearner physical aspects of the learner 28-1. The physical aspectsincludes a physical condition of the learner and physical movements ofthe learner, including one or more of gait of walking information, paceof movement, range of movement, relative height, relative volume, anyphysical aspect, etc.

FIG. 18C further illustrates the example of operation of the method tooptimize learning, where having received the environment sensorinformation and generated the learner interaction information, in afourth step the experience execution module 32 analyzes the learnerinteraction information and the environmental sensor information basedon the assessment information and learner profile information to producelearner identification information (e.g., who it really is). Forexample, the learning assessment module 330 analyzes the learnerinteraction information 332 and the environment sensor information 150based on the assessment information 252 and the learner profileinformation 868 (e.g., guidance for movement patterns of variouslearners) to produce learner identification information 870. The learneridentification information 870 is correlated to the learner physicalaspects. For instance, the learner identification information 870identifies the learner 28-1 versus a multitude of other potentiallearners based on the physical aspects of the learner 28-1 whilereacting to the execution of the learning path.

FIG. 18D further illustrates the example of operation of the method tooptimize learning, where having produced the learner identificationinformation, in a fifth step the experience execution module 32 selectsan adaptation of a portion of the lesson package in accordance with thelearner identification information. The adaptation includes re-selectingthe first portion (e.g., a redo) with or without enhancement for thepresent learner, selecting a different portion (e.g., a next learningobject along the sequence of the learning path) for the present learner,and selecting a different portion associated with another learningobject along a different sequence of a variation to the learning path.The adaptation further includes providing an updated assessmentquestion, changing the pace (e.g., slower when the present learner has amore effective learning experience at a slower pace, faster when thepresent learner has a more effective learning experience at a fasterpace) of execution of the learning objects along the learning path.

Having selected the adaptation, in a sixth step the experience executionmodule 32 outputs a representation of the adaptation of the portion ofthe lesson package (e.g., to the same learner). For example, theinstance experience module 290 selects a different learning object froman alternative learning path, generates enhanced learner outputinformation 860 based on assets of the different learning object, andoutputs the enhanced learning output information 860 to the learner28-1.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, text, graphics, audio, etc. any of which may generally bereferred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. For some industries, anindustry-accepted tolerance is less than one percent and, for otherindustries, the industry-accepted tolerance is 10 percent or more. Otherexamples of industry-accepted tolerance range from less than one percentto fifty percent. Industry-accepted tolerances correspond to, but arenot limited to, component values, integrated circuit process variations,temperature variations, rise and fall times, thermal noise, dimensions,signaling errors, dropped packets, temperatures, pressures, materialcompositions, and/or performance metrics. Within an industry, tolerancevariances of accepted tolerances may be more or less than a percentagelevel (e.g., dimension tolerance of less than +/−1%). Some relativitybetween items may range from a difference of less than a percentagelevel to a few percent. Other relativity between items may range from adifference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operablycoupled to”, “coupled to”, and/or “coupling” includes direct couplingbetween items and/or indirect coupling between items via an interveningitem (e.g., an item includes, but is not limited to, a component, anelement, a circuit, and/or a module) where, for an example of indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operableto”, “coupled to”, or “operably coupled to” indicates that an itemincludes one or more of power connections, input(s), output(s), etc., toperform, when activated, one or more its corresponding functions and mayfurther include inferred coupling to one or more other items. As maystill further be used herein, the term “associated with”, includesdirect and/or indirect coupling of separate items and/or one item beingembedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing circuitry”, and/or “processing unit”may be a single processing device or a plurality of processing devices.Such a processing device may be a microprocessor, micro-controller,digital signal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, processing circuitry, and/or processing unitmay be, or further include, memory and/or an integrated memory element,which may be a single memory device, a plurality of memory devices,and/or embedded circuitry of another processing module, module,processing circuit, processing circuitry, and/or processing unit. Such amemory device may be a read-only memory, random access memory, volatilememory, non-volatile memory, static memory, dynamic memory, flashmemory, cache memory, and/or any device that stores digital information.Note that if the processing module, module, processing circuit,processing circuitry, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,processing circuitry and/or processing unit implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, processing circuitry and/or processing unitexecutes, hard coded and/or operational instructions corresponding to atleast some of the steps and/or functions illustrated in one or more ofthe Figures. Such a memory device or memory element can be included inan article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with one or more other routines. In addition, a flow diagrammay include an “end” and/or “continue” indication. The “end” and/or“continue” indications reflect that the steps presented can end asdescribed and shown or optionally be incorporated in or otherwise usedin conjunction with one or more other routines. In this context, “start”indicates the beginning of the first step presented and may be precededby other activities not specifically shown. Further, the “continue”indication reflects that the steps presented may be performed multipletimes and/or may be succeeded by other activities not specificallyshown. Further, while a flow diagram indicates a particular ordering ofsteps, other orderings are likewise possible provided that theprinciples of causality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, a quantum register or otherquantum memory and/or any other device that stores data in anon-transitory manner. Furthermore, the memory device may be in a formof a solid-state memory, a hard drive memory or other disk storage,cloud memory, thumb drive, server memory, computing device memory,and/or other non-transitory medium for storing data. The storage of dataincludes temporary storage (i.e., data is lost when power is removedfrom the memory element) and/or persistent storage (i.e., data isretained when power is removed from the memory element). As used herein,a transitory medium shall mean one or more of: (a) a wired or wirelessmedium for the transportation of data as a signal from one computingdevice to another computing device for temporary storage or persistentstorage; (b) a wired or wireless medium for the transportation of dataas a signal within a computing device from one element of the computingdevice to another element of the computing device for temporary storageor persistent storage; (c) a wired or wireless medium for thetransportation of data as a signal from one computing device to anothercomputing device for processing the data by the other computing device;and (d) a wired or wireless medium for the transportation of data as asignal within a computing device from one element of the computingdevice to another element of the computing device for processing thedata by the other element of the computing device. As may be usedherein, a non-transitory computer readable memory is substantiallyequivalent to a computer readable memory. A non-transitory computerreadable memory can also be referred to as a non-transitory computerreadable storage medium.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for creating a multi-disciplinedlearning tool regarding a topic, the method comprises: obtaining, by acomputing entity, a first learner approach associated with a firstlearner of a set of learners; obtaining, by the computing entity, afirst learning object based on the first learner approach regarding thetopic, wherein the first learning object includes a first set ofknowledge bullet-points for a first piece of information regarding thetopic; obtaining, by the computing entity, a second learning objectbased on the first learner approach regarding the topic, wherein thesecond learning object includes a second set of knowledge bullet-pointsfor a second piece of information regarding the topic, wherein at leastone knowledge bullet-point of the second set of knowledge bullet-pointsis different than each knowledge bullet-point of the first set ofknowledge bullet-points; creating, by the computing entity, first lessonasset information regarding the topic for the first learner based on thefirst learner approach associated with the first learner, wherein thefirst lesson asset information includes the first learning object andthe second learning object; determining, by the computing entity, afirst set of assets to represent the first learning object and a secondset of assets to represent the second learning object, wherein eachasset of the first and second sets of assets is capable of beingrendered to produce associated digital video frames; identifying, by thecomputing entity, a first asset of the first set of assets that is thesame as a second asset of the second set of assets to produce a commonillustrative asset; rendering, by the computing entity, the commonillustrative asset based on at least one specific instance of commonillustrative asset portrayal of at least some of the first and secondsets of knowledge bullet-points to produce a set of digital video framesof the common illustrative asset; selecting, by the computing entity, afirst subset of the set of digital video frames of the commonillustrative asset to produce a portion of first descriptive assetdigital video frames, wherein first descriptive asset digital videoframes fully represent the first learning object, wherein the portion ofthe first descriptive asset digital video frames represents portrayal ofan aspect of the first set of knowledge bullet-points; rendering, by thecomputing entity, the first set of assets to represent portrayal ofremaining aspects of the first set of knowledge bullet-points to producea remaining portion of the first descriptive asset digital video framesto provide completion of the first descriptive asset digital videoframes; selecting, by the computing entity, a second subset of the setof digital video frames of the common illustrative asset to produce aportion of second descriptive asset digital video frames, wherein seconddescriptive asset digital video frames fully represent the secondlearning object, wherein the portion of the second descriptive assetdigital video frames represents portrayal of an aspect of the second setof knowledge bullet-points; rendering, by the computing entity, thesecond set of assets to represent portrayal of remaining aspects of thesecond set of knowledge bullet-points to produce a remaining portion ofthe second descriptive asset digital video frames to provide completionof the second descriptive asset digital video frames; and outputting, bythe computing entity, the first descriptive asset digital video framesand the second descriptive asset digital video frames as the firstlesson asset information to a first learner computing entity associatedwith the first learner for interactive consumption.
 2. The method ofclaim 1 further comprises: obtaining, by the computing entity, a secondlearner approach associated with a second learner of the set oflearners, wherein the second learner approach is different than thefirst learner approach; obtaining, by the computing entity, a thirdlearning object based on the second learner approach regarding thetopic, wherein the third learning object includes a third set ofknowledge bullet-points for the first piece of information regarding thetopic; obtaining, by the computing entity, a fourth learning objectbased on the second learner approach regarding the topic, wherein thefourth learning object includes a fourth set of knowledge bullet-pointsfor the second piece of information regarding the topic, wherein atleast one knowledge bullet-point of the fourth set of knowledgebullet-points is different than each knowledge bullet-point of the thirdset of knowledge bullet-points; creating, by the computing entity,second lesson asset information regarding the topic for the secondlearner based on the second learner approach associated with the secondlearner, wherein the second lesson asset information includes the thirdlearning object and the fourth learning object; determining, by thecomputing entity, a third set of assets to represent the third learningobject and a fourth set of assets to represent the fourth learningobject, wherein each asset of the third and fourth sets of assets iscapable of being rendered to produce associated digital video frames;identifying, by the computing entity, a third asset of the third set ofassets that is the same as a fourth asset of the fourth set of assets toproduce another common illustrative asset; rendering, by the computingentity, the other common illustrative asset based on at least onespecific instance of other common illustrative asset portrayal of atleast some of the third and fourth sets of knowledge bullet-points toproduce a set of digital video frames of the other common illustrativeasset; selecting, by the computing entity, a third subset of the set ofdigital video frames of the other common illustrative asset to produce aportion of third descriptive asset digital video frames, wherein thirddescriptive asset digital video frames fully represent the thirdlearning object, wherein the portion of the third descriptive assetdigital video frames represents portrayal of an aspect of the third setof knowledge bullet-points; rendering, by the computing entity, thethird set of assets to represent portrayal of remaining aspects of thethird set of knowledge bullet-points to produce a remaining portion ofthe third descriptive asset digital video frames to provide completionof the third descriptive asset digital video frames; selecting, by thecomputing entity, a fourth subset of the set of digital video frames ofthe other common illustrative asset to produce a portion of fourthdescriptive asset digital video frames, wherein fourth descriptive assetdigital video frames fully represent the fourth learning object, whereinthe portion of the fourth descriptive asset digital video framesrepresents portrayal of an aspect of the fourth set of knowledgebullet-points; rendering, by the computing entity, the fourth set ofassets to represent portrayal of remaining aspects of the fourth set ofknowledge bullet-points to produce a remaining portion of the fourthdescriptive asset digital video frames to provide completion of thefourth descriptive asset digital video frames; and outputting, by thecomputing entity, the third descriptive asset digital video frames andthe fourth descriptive asset digital video frames as the second lessonasset information to a second learner computing entity associated withthe second learner for further interactive consumption.
 3. The method ofclaim 1, wherein the obtaining the first learner approach associatedwith the first learner of the set of learners comprises at least one of:establishing the first learner approach to exclude utilization of aparticular knowledge bullet-point from inclusion in the first set ofknowledge bullet-points and the second set of knowledge bullet-pointswhen the first learner does not have sufficient authorization to accessthe particular knowledge bullet-point; establishing the first learnerapproach to modify the particular knowledge bullet-point to produce amodified knowledge bullet-point for inclusion in at least one of thefirst set of knowledge bullet-points and the second set of knowledgebullet-points; establishing the first learner approach to includeutilization of a first expansion knowledge bullet-point in the first setof knowledge bullet-points for an expansion first piece of informationassociated with the first piece of information regarding the topic;establishing the first learner approach to include utilization of asecond expansion knowledge bullet-point in the second set of knowledgebullet-points for an expansion second piece of information associatedwith the second piece of information regarding the topic; establishingthe first learner approach to exclude utilization of a particular assetas the common illustrative asset when the first learner does not havesufficient authorization to access the particular asset; andestablishing the first learner approach to modify the particular assetto produce a modified asset for inclusion as the common illustrativeasset.
 4. A computing device comprises: an interface; a local memory;and a processing module operably coupled to the interface and the localmemory, wherein the processing module functions to: obtain a firstlearner approach associated with a first learner of a set of learners;obtain a first learning object based on the first learner approachregarding the topic, wherein the first learning object includes a firstset of knowledge bullet-points for a first piece of informationregarding the topic; obtain a second learning object based on the firstlearner approach regarding the topic, wherein the second learning objectincludes a second set of knowledge bullet-points for a second piece ofinformation regarding the topic, wherein at least one knowledgebullet-point of the second set of knowledge bullet-points is differentthan each knowledge bullet-point of the first set of knowledgebullet-points; create first lesson asset information regarding the topicfor the first learner based on the first learner approach associatedwith the first learner, wherein the first lesson asset informationincludes the first learning object and the second learning object;determine a first set of assets to represent the first learning objectand a second set of assets to represent the second learning object,wherein each asset of the first and second sets of assets is capable ofbeing rendered to produce associated digital video frames; identify afirst asset of the first set of assets that is the same as a secondasset of the second set of assets to produce a common illustrativeasset; render the common illustrative asset based on at least onespecific instance of common illustrative asset portrayal of at leastsome of the first and second sets of knowledge bullet-points to producea set of digital video frames of the common illustrative asset; select afirst subset of the set of digital video frames of the commonillustrative asset to produce a portion of first descriptive assetdigital video frames, wherein first descriptive asset digital videoframes fully represent the first learning object, wherein the portion ofthe first descriptive asset digital video frames represents portrayal ofan aspect of the first set of knowledge bullet-points; render the firstset of assets to represent portrayal of remaining aspects of the firstset of knowledge bullet-points to produce a remaining portion of thefirst descriptive asset digital video frames to provide completion ofthe first descriptive asset digital video frames; select a second subsetof the set of digital video frames of the common illustrative asset toproduce a portion of second descriptive asset digital video frames,wherein second descriptive asset digital video frames fully representthe second learning object, wherein the portion of the seconddescriptive asset digital video frames represents portrayal of an aspectof the second set of knowledge bullet-points; render the second set ofassets to represent portrayal of remaining aspects of the second set ofknowledge bullet-points to produce a remaining portion of the seconddescriptive asset digital video frames to provide completion of thesecond descriptive asset digital video frames; and output, via theinterface, the first descriptive asset digital video frames and thesecond descriptive asset digital video frames as the first lesson assetinformation to a first learner computing entity associated with thefirst learner for interactive consumption.
 5. The computing device ofclaim 4, wherein the processing module further functions to: obtain asecond learner approach associated with a second learner of the set oflearners, wherein the second learner approach is different than thefirst learner approach; obtain a third learning object based on thesecond learner approach regarding the topic, wherein the third learningobject includes a third set of knowledge bullet-points for the firstpiece of information regarding the topic; obtain a fourth learningobject based on the second learner approach regarding the topic, whereinthe fourth learning object includes a fourth set of knowledgebullet-points for the second piece of information regarding the topic,wherein at least one knowledge bullet-point of the fourth set ofknowledge bullet-points is different than each knowledge bullet-point ofthe third set of knowledge bullet-points; create second lesson assetinformation regarding the topic for the second learner based on thesecond learner approach associated with the second learner, wherein thesecond lesson asset information includes the third learning object andthe fourth learning object; determine a third set of assets to representthe third learning object and a fourth set of assets to represent thefourth learning object, wherein each asset of the third and fourth setsof assets is capable of being rendered to produce associated digitalvideo frames; identify a third asset of the third set of assets that isthe same as a fourth asset of the fourth set of assets to produceanother common illustrative asset; render the other common illustrativeasset based on at least one specific instance of other commonillustrative asset portrayal of at least some of the third and fourthsets of knowledge bullet-points to produce a set of digital video framesof the other common illustrative asset; select a third subset of the setof digital video frames of the other common illustrative asset toproduce a portion of third descriptive asset digital video frames,wherein third descriptive asset digital video frames fully represent thethird learning object, wherein the portion of the third descriptiveasset digital video frames represents portrayal of an aspect of thethird set of knowledge bullet-points; render the third set of assets torepresent portrayal of remaining aspects of the third set of knowledgebullet-points to produce a remaining portion of the third descriptiveasset digital video frames to provide completion of the thirddescriptive asset digital video frames; select a fourth subset of theset of digital video frames of the other common illustrative asset toproduce a portion of fourth descriptive asset digital video frames,wherein fourth descriptive asset digital video frames fully representthe fourth learning object, wherein the portion of the fourthdescriptive asset digital video frames represents portrayal of an aspectof the fourth set of knowledge bullet-points; render the fourth set ofassets to represent portrayal of remaining aspects of the fourth set ofknowledge bullet-points to produce a remaining portion of the fourthdescriptive asset digital video frames to provide completion of thefourth descriptive asset digital video frames; and output, via theinterface, the third descriptive asset digital video frames and thefourth descriptive asset digital video frames as the second lesson assetinformation to a second learner computing entity associated with thesecond learner for further interactive consumption.
 6. The computingdevice of claim 4, wherein the processing module functions to obtain thefirst learner approach associated with the first learner of the set oflearners by at least one of: establishing the first learner approach toexclude utilization of a particular knowledge bullet-point frominclusion in the first set of knowledge bullet-points and the second setof knowledge bullet-points when the first learner does not havesufficient authorization to access the particular knowledgebullet-point; establishing the first learner approach to modify theparticular knowledge bullet-point to produce a modified knowledgebullet-point for inclusion in at least one of the first set of knowledgebullet-points and the second set of knowledge bullet-points;establishing the first learner approach to include utilization of afirst expansion knowledge bullet-point in the first set of knowledgebullet-points for an expansion first piece of information associatedwith the first piece of information regarding the topic; establishingthe first learner approach to include utilization of a second expansionknowledge bullet-point in the second set of knowledge bullet-points foran expansion second piece of information associated with the secondpiece of information regarding the topic; establishing the first learnerapproach to exclude utilization of a particular asset as the commonillustrative asset when the first learner does not have sufficientauthorization to access the particular asset; and establishing the firstlearner approach to modify the particular asset to produce a modifiedasset for inclusion as the common illustrative asset.
 7. Anon-transitory computer readable memory comprises: a first memoryelement that stores operational instructions that, when executed by aprocessing module, causes the processing module to: obtain a firstlearner approach associated with a first learner of a set of learners;and a second memory element that stores operational instructions that,when executed by the processing module, causes the processing module to:obtain a first learning object based on the first learner approachregarding the topic, wherein the first learning object includes a firstset of knowledge bullet-points for a first piece of informationregarding the topic; obtain a second learning object based on the firstlearner approach regarding the topic, wherein the second learning objectincludes a second set of knowledge bullet-points for a second piece ofinformation regarding the topic, wherein at least one knowledgebullet-point of the second set of knowledge bullet-points is differentthan each knowledge bullet-point of the first set of knowledgebullet-points; create first lesson asset information regarding the topicfor the first learner based on the first learner approach associatedwith the first learner, wherein the first lesson asset informationincludes the first learning object and the second learning object;determine a first set of assets to represent the first learning objectand a second set of assets to represent the second learning object,wherein each asset of the first and second sets of assets is capable ofbeing rendered to produce associated digital video frames; identify afirst asset of the first set of assets that is the same as a secondasset of the second set of assets to produce a common illustrativeasset; render the common illustrative asset based on at least onespecific instance of common illustrative asset portrayal of at leastsome of the first and second sets of knowledge bullet-points to producea set of digital video frames of the common illustrative asset; select afirst subset of the set of digital video frames of the commonillustrative asset to produce a portion of first descriptive assetdigital video frames, wherein first descriptive asset digital videoframes fully represent the first learning object, wherein the portion ofthe first descriptive asset digital video frames represents portrayal ofan aspect of the first set of knowledge bullet-points; render the firstset of assets to represent portrayal of remaining aspects of the firstset of knowledge bullet-points to produce a remaining portion of thefirst descriptive asset digital video frames to provide completion ofthe first descriptive asset digital video frames; select a second subsetof the set of digital video frames of the common illustrative asset toproduce a portion of second descriptive asset digital video frames,wherein second descriptive asset digital video frames fully representthe second learning object, wherein the portion of the seconddescriptive asset digital video frames represents portrayal of an aspectof the second set of knowledge bullet-points; render the second set ofassets to represent portrayal of remaining aspects of the second set ofknowledge bullet-points to produce a remaining portion of the seconddescriptive asset digital video frames to provide completion of thesecond descriptive asset digital video frames; and output the firstdescriptive asset digital video frames and the second descriptive assetdigital video frames as the first lesson asset information to a firstlearner computing entity associated with the first learner forinteractive consumption.
 8. The computer readable memory of claim 7further comprises: a third memory element stores operationalinstructions that, when executed by the processing module, causes theprocessing module to: obtain a second learner approach associated with asecond learner of the set of learners, wherein the second learnerapproach is different than the first learner approach; obtain a thirdlearning object based on the second learner approach regarding thetopic, wherein the third learning object includes a third set ofknowledge bullet-points for the first piece of information regarding thetopic; obtain a fourth learning object based on the second learnerapproach regarding the topic, wherein the fourth learning objectincludes a fourth set of knowledge bullet-points for the second piece ofinformation regarding the topic, wherein at least one knowledgebullet-point of the fourth set of knowledge bullet-points is differentthan each knowledge bullet-point of the third set of knowledgebullet-points; create second lesson asset information regarding thetopic for the second learner based on the second learner approachassociated with the second learner, wherein the second lesson assetinformation includes the third learning object and the fourth learningobject; determine a third set of assets to represent the third learningobject and a fourth set of assets to represent the fourth learningobject, wherein each asset of the third and fourth sets of assets iscapable of being rendered to produce associated digital video frames;identify a third asset of the third set of assets that is the same as afourth asset of the fourth set of assets to produce another commonillustrative asset; render the other common illustrative asset based onat least one specific instance of other common illustrative assetportrayal of at least some of the third and fourth sets of knowledgebullet-points to produce a set of digital video frames of the othercommon illustrative asset; select a third subset of the set of digitalvideo frames of the other common illustrative asset to produce a portionof third descriptive asset digital video frames, wherein thirddescriptive asset digital video frames fully represent the thirdlearning object, wherein the portion of the third descriptive assetdigital video frames represents portrayal of an aspect of the third setof knowledge bullet-points; render the third set of assets to representportrayal of remaining aspects of the third set of knowledgebullet-points to produce a remaining portion of the third descriptiveasset digital video frames to provide completion of the thirddescriptive asset digital video frames; select a fourth subset of theset of digital video frames of the other common illustrative asset toproduce a portion of fourth descriptive asset digital video frames,wherein fourth descriptive asset digital video frames fully representthe fourth learning object, wherein the portion of the fourthdescriptive asset digital video frames represents portrayal of an aspectof the fourth set of knowledge bullet-points; render the fourth set ofassets to represent portrayal of remaining aspects of the fourth set ofknowledge bullet-points to produce a remaining portion of the fourthdescriptive asset digital video frames to provide completion of thefourth descriptive asset digital video frames; and output the thirddescriptive asset digital video frames and the fourth descriptive assetdigital video frames as the second lesson asset information to a secondlearner computing entity associated with the second learner for furtherinteractive consumption.
 9. The computer readable memory of claim 7,wherein the processing module functions to execute the operationalinstructions stored by the first memory element to cause the processingmodule to obtain the first learner approach associated with the firstlearner of the set of learners by at least one of: establishing thefirst learner approach to exclude utilization of a particular knowledgebullet-point from inclusion in the first set of knowledge bullet-pointsand the second set of knowledge bullet-points when the first learnerdoes not have sufficient authorization to access the particularknowledge bullet-point; establishing the first learner approach tomodify the particular knowledge bullet-point to produce a modifiedknowledge bullet-point for inclusion in at least one of the first set ofknowledge bullet-points and the second set of knowledge bullet-points;establishing the first learner approach to include utilization of afirst expansion knowledge bullet-point in the first set of knowledgebullet-points for an expansion first piece of information associatedwith the first piece of information regarding the topic; establishingthe first learner approach to include utilization of a second expansionknowledge bullet-point in the second set of knowledge bullet-points foran expansion second piece of information associated with the secondpiece of information regarding the topic; establishing the first learnerapproach to exclude utilization of a particular asset as the commonillustrative asset when the first learner does not have sufficientauthorization to access the particular asset; and establishing the firstlearner approach to modify the particular asset to produce a modifiedasset for inclusion as the common illustrative asset.